Explorations into the nature of reality have been undertaken across the ages, and in the contemporary world, disparate tools, from *gedanken* experiments [1-4], experimental consistency checks [5,6] to machine learning and artificial intelligence are being used to illuminate the fundamental layers of reality [7]. A theory of everything, a grand unified theory of physics and nature, has been elusive for the world of Physics.

While unifying various forces and interactions in nature, starting from the unification of electricity and magnetism in James Clerk Maxwell’s seminal work *A Treatise on Electricity and Magnetism* [8] to the electroweak unification by Weinberg-Salam-Glashow [9-11] and research in the direction of establishing the Standard Model including the QCD sector by Murray Gell-Mann and Richard Feynman [12,13], has seen developments in a slow but surefooted manner, we now have a few candidate theories of everything, primary among which is String Theory [14].

Unfortunately, we are still some way off from establishing various areas of the theory in an empirical manner. Chief among this is the concept of supersymmetry [15], which is an important part of String Theory. There were no evidences found for supersymmetry in the first run of the Large Hadron Collider [16].

When the Large Hadron Collider discovered the Higgs Boson in 2011-12 [17-19], there were results that were problematic for the *Minimum Supersymmetric Model (MSSM)*, since the value of the mass of the Higgs Boson at 125 GeV is relatively large for the model and could only be attained with large radiative loop corrections from top squarks that many theoreticians considered to be `unnatural’ [20].

In the absence of experiments that can test certain frontiers of Physics, particularly due to energy constraints particularly at the smallest of scales, the importance of simulations and computational research cannot be underplayed. Gone are the days when Isaac Newton purportedly could sit below an apple tree and infer the concept of classical gravity from an apple that had fallen on his head.

In today’s age, we have increasing levels of computational inputs and power that factor in when considering avenues of new research in Physics. For instance, M-Theory, introduced by Edward Witten in 1995 [21], is a promising approach to a unified model of Physics that includes quantum gravity. It extends the formalism of String Theory.

There have been computational tools relating to machine learning that have lately been used for solving M-Theory geometries [22]. TensorFlow, a computing platform normally used for machine learning, helped in finding 194 equilibrium solutions for one particular type of M-Theory spacetime geometries [23-25].

Artificial intelligence has been one of the primary areas of interest in computational pursuits around Physics research. In 2020, Matsubara Takashi (Osaka University) and Yaguchi Takaharu (Kobe University), along with their research group, were successful in developing technology that could simulate phenomena for which we do not have the detailed formula or mechanism, using artificial intelligence [26].

The underlying step here is the creation of a model from observational data, constrained by the model being consistent and faithful to the laws of Physics. In this pursuit, the researchers utilized digital calculus as well as geometrical approach, such as those of Riemannian geometry and symplectic geometry.

For the former, certain novel digital versions of *backpropagation* (an algorithm used in machine learning that is used to calculate how one could best correct incorrect responses given by artificial intelligence during the learning period based on differentiation calculations) using automatic differentiation were used [27]. Using this new approach, one could implement and preserve physical laws such as energy conservation. NASA has also used artificial intelligence to find an eighth planet (*Kepler-90i*) circling Kepler-90, a Sun-like star that is 2545 light-years away from Earth recently [28].

This discovery came about which researchers Andrew Vanderburg and Christopher Shallue trained neural networks to identify transiting exoplanets [29,30]. For this they used a set of 15,000 signals, which had been vetted previously, from the Kepler exoplanet catalogue, based on which they trained a computer to learn how to identify exoplanets in these signals.

The artificial `neural network’ analysed the Kepler data and found weak transit signals from Kepler-90i, after searching for weaker signals in 670 star systems. Recently, new analytical techniques have also been used to constrain effective field theories in experiments in the Large Hadron Collider. Using this experimental data and those from Monte Carlo simulations, we can train neural networks to estimate likelihood ratios for use in limit setting procedures [31,32].

The approaches have the advantage of scaling well to large-scale analyses with higher-dimensional parameter spaces and many observables. They also do not require approximations, such as those of parton showers, hard processes or detector effects, besides the likelihood ratio being able to evaluated in microseconds.

In this way, utilising artificial intelligence, we can obtain a high-performance, practical and scalable approach to improve the legacy measurements of the Large Hadron Collider. Artificial intelligence has already been applied successfully to varied scientific fields and problems, such as in tensor networks, for error-correction algorithms and searching for new states of quantum matter [33].

Artificial intelligence and neural networks were instrumental in finding the Higgs Boson. The data from the Large Hadron Collider has millions of data each day, analyzing which is a tedious process, besides particles like the Higgs boson lie in the noise of this data [34,35]. A quantum computer processor known as the annealer helped the Large Hadron Collider to detect the particle, using patterns in particle collisions [36,37].

*Convolutional Neural Network (CNN)* architecture can be used to tackle the problem of identifying the particle from colour-flow energy images [38,39]. Current methods to employ conceptual tools like the *Fisher Discriminant Analysis* on data of jet pull superstructures [40]. It is extremely tough to study the collisions and particles during the operation of the LHC since the half-lifetimes of the particles are small and they decay quickly.

Without artificial intelligence, this discovery may have been impossible! Artificial intelligence has been used in undertaking another seminal experiment – the creation of Bose-Einstein condensate (BEC) [41-46]. In this experiment, the gas was cooled to around 1 microkelvin and thereafter the control of the lasers used was given to the artificial intelligence system that was tasked with cooling the trapped gas to temperatures typically in the order of nanokelvins.

The artificial intelligence system could undertake this experiment in a span of less than one hour. Usually, Bose-Einstein condensate is produced using evaporative cooling. Microscopic semiclassical theory, while capable of describing the process, may oversimplify the dynamics and miss more effective and complex methods of evaporation, for instance the finding that circumvention of higher-order inelastic collisions can produce large condensates.

We can now automate the process of the creation of BEC using Machine-learning Online Optimization (MLOO) [46]. We can also create internal models that can predict the performance of future experiments for any set of parameters, using Gaussian processes. Such algorithms can fit models to previous observations and choose the possible future experiments that can best refine the model.

Artificial intelligence helps at the cosmological scale as well, wherein AI has been used to find gravitational wave signatures using deep learning algorithms [47-50]. We can also employ convolutional neural networks to find gravitational lensing in astrophysical data. Recently, the image of a black hole was an example of utilization of machine learning in astrophysics [51-55].

Most gravitational lensing events are subtle, which result in partial arcs or smears, for which artificial intelligence can help with identifying such elements. Machine learning can fasten the simulations for the formation of galaxies, besides being able to extrapolate existing simulations of cosmological structures using neural networks [56-58].

Such techniques have recently been used to match the amount of visible matter to the amount of dark matter in galaxies [59-61]. We can also use neural networks to reconstructs when our atmosphere is hit by cosmic rays [62]. In atmospheric physics, we use algorithms in artificial intelligence such as decision trees, fuzzy logic and neural networks [63,64].

This has a practical aspect, with problems such as the understanding of the mechanism of various kinds of pollution [65]. We can also identify cyclones using self-organizing maps and clustering [66].

Artificial intelligence has been used for describing elements and phenomena in Nuclear Physics, with neural networks being able to represent ground state wavefunctions [67-70]. Using algorithms for artificial intelligence, such as neural networks, we have developed models nuclear physics properties like ground state parities and spin, atomic mass number, neutron capture rates, neutron separation energies, branching probabilities in disparate decay channels as well as beta-decay half-lives, to determine the properties of halo nuclear and exotic systems [71-75].

In the recent past, neural networks have also helped in determining heavy quarks as well as identifying electrons [76]. We can use deep learning to solve Schrodinger’s equation, to find the ground state energy [77]. There is a growing need to turn noisy and large data sets into meaningful information as we try to increase our ability to prepare and control increasingly complex quantum systems experimentally.

It is in this area that we can utilize machine learning, such as the use of algorithmic learning and Bayesian methods for Hamiltonian learning [78], to classify quantum states [79] and to characterize unknown unitary transformations [80].

Reconstruction of the Hamiltonian to identify an accurate model for quantum system dynamics, extracting information on unknown quantum states and engineering quantum gates with pairwise interactions, using both time-independent and time-dependent hamiltonians, are all better done using artificial intelligence. We use quantum machine learning to accelerate the prediction of properties of molecules and materials, and this can be used in-turn to computationally design new molecules and materials [81,82].

We can infer molecular atomization energies [83], generate new quantum experiments [84], study accurate potential energy surfaces [85] with restricted Boltzmann machines, interpolate interatomic potentials [86], identify phase transitions [87] from entanglement spectrum and generate adaptive feedback schemes for quantum tomography [88] and quantum metrology [89] using machine learning techniques.

We can efficiently generate efficient optimization functions using what are known as variational functions, which are a family of algorithms that utilize training based on an objective function and on circuit parameters. In quantum physics, we can avoid the sign problem for integration for path integrals by finding a better manifold for the integration using machine learning techniques [90].

Speaking of the quantum domain, quantum computing can accelerate deep learning algorithms [91], such as Convolutional Neural Networks, with quantum computing potentially being applied to simulation, optimization and sampling. Major technology companies such as *Rigetti* [92] and *Google* (that, along with NASA and Universities Space Research Association, set up Quantum Artificial Intelligence Lab in 2013 [93]) are researching Quantum AI and full stack quantum computing.

In material sciences, artificial intelligence has been used to find glass-forming systems, besides being used in different matters, such as in Nanosciences. Semiconductors lie between pure conductors and insulators, in conductivity, and constitute important components in various computing devices, which have been seeing generation-after-generation of evolution in terms of affordability and size.

We require high-precision machines designing for simulating, studying and designing novel kinds of semiconductor systems and processes. Methods around neural network can provide us with an effective way to analyse extreme properties of semiconductors [94].

Recently, a Chinese group of researchers studied deterioration of semiconductor equipment, with the employment of backpropagation neural network model [95] (and the MATLAB Neural Network Toolbox for analysis) as well as grey relation analysis [96] in simultaneous fault detection and classification [97] for semiconductor manufacturing tools, after using the Novellus Vector Machine and its Remote Process Controller (RPC) function [98].

For this process, one undertakes data preprocessing, setting of network variables, choosing input variables, determination of hidden layer neuron, sensitivity analysis and network output results determining principles.

The artificial neural network is created by considering all the relevant factors such as learning trials, momentum correction coefficient and learning rate, which are suitably chosen to accommodate variations in the neural networks. The number of hidden layers of the network is found using the re-selection of previous variables obtained in the experiment.

Johannes Kepler revolutionised science by discovering that the orbit of Mars is an ellipse at the turn of the seventeenth century. He did so by fitting the data tables on planetary orbits, initially to varied ovoid shapes and finally to an ellipse, over a period of four years [99]. This was a prime example of symbolic regression, which refers to the matching of a symbolic expression that accurately marches a given dataset.

As much this has formed the basis of scientific discoveries and breakthroughs in the past, it also poses a core challenge for artificial intelligence as well as Physics. Using symmetries, compositionality, separability and other simplifying properties one can approach and possibly solve a problem that is otherwise often regarded as NP-hard.

Recently, there was the development of a multidimensional, recursive symbolic regression algorithm that combines the fitting of neural networks with a set of physics-inspired techniques, particularly 100 equations from the Feynman Lectures on Physics [100].

While approaches with other publicly available software could discover only 71, this new application of neural networks could discover all of them. While artificial intelligence frameworks, particularly convolutional neural networks, can learn patterns in two-dimensional date quite well. Examples include recognizing objects in digital images as well as handwritten words.

However, such machine learning architectures are not as successful when the data sets are without any associated built-in planar geometry, such as point clouds that are generated by self-driving cars used in three-dimensional computer animation. To bypass this, geometric deep learning, a new discipline of artificial intelligence, emerged around 2016 [101].

With the new framework and what are known as gauge-equivalent convolutional neural networks (gauge CNNs), one can build neural networks that can learn patterns on any type of geometric surface, including curved surfaces.

These developments – extension of artificial intelligence, machine learning and neural network to higher dimensions as well as generalized geometric surfaces are of prime importance for advancement of research in frontier Physics, even as we look at solving the conundrum of Quantum Gravity using approaches such as geometrodynamics and causal dynamical triangulation.

The sky is the limit for the application and relevance of artificial intelligence to probe, analyse, study and find the fundamental elements and nuances of reality in the Universe.

**References:**

[1] Aspect, A., Grangier, P., & Roger, G. (1982). Experimental realization of Einstein-Podolsky-Rosen-Bohm Gedankenexperiment: a new violation of Bell’s inequalities. Physical review letters, 49(2), 91.

[2] Jacques, V., Wu, E., Grosshans, F., Treussart, F., Grangier, P., Aspect, A., & Roch, J. F. (2007). Experimental realization of Wheeler’s delayed-choice gedanken experiment. Science, 315(5814), 966-968.

[3] Scardigli, F. (1999). Generalized uncertainty principle in quantum gravity from micro-black hole gedanken experiment. Physics Letters B, 452(1-2), 39-44.

[4] Liu, X. J., Miao, Q., Gel’Mukhanov, F., Patanen, M., Travnikova, O., Nicolas, C., … & Miron, C. (2015). Einstein–Bohr recoiling double-slit gedanken experiment performed at the molecular level. Nature Photonics, 9(2), 120-125.

[5] Amaldo, U., de Boer, W., Frampton, P. H., Fürstenau, H., & Liu, J. T. (1992). Consistency checks of grand unified theories. Physics Letters B, 281(3-4), 374-382.

[6] De Boer, W. (1994). Grand unified theories and supersymmetry in particle physics and cosmology. Progress in Particle and Nuclear Physics, 33, 201-301.

[7] Hogg, T., & Huberman, B. A. (1987). Artificial intelligence and large scale computation: A physics perspective. Physics Reports, 156(5), 227-310.

[8] Maxwell, J. C. (1873). A treatise on electricity and magnetism (Vol. 1). Oxford: Clarendon Press.

[9] Glashow, S. L. (1959). The renormalizability of vector meson interactions. Nuclear Physics, 10, 107-117.

[10] Salam, A., & Ward, J. C. (1961). Weak and Electromagnetic Interactions, Novo Cim. 11 (1959) 568. On a Gauge Group of Elementary Interactions, ibid, 19, 165.

[11] Weinberg, S. (1967). A model of leptons. Physical review letters, 19(21), 1264.

[12] Gell-Mann, M. (1961). The Eightfold Way: A Theory of strong interaction symmetry (No. TID-12608; CTSL-20). California Inst. of Tech., Pasadena. Synchrotron Lab..

[13] Feynman, R. P. (1969). High Energy Collisions, Third International Conference at Stony Brook, NY.

[14] Becker, K., Becker, M., & Schwarz, J. H. (2006). String theory and M-theory: A modern introduction. Cambridge university press.

[15] Fayet, P., & Ferrara, S. (1977). Supersymmetry. Physics Reports, 32(5), 249-334.

[16] Canepa, A. (2019). Searches for supersymmetry at the Large Hadron Collider. Reviews in Physics, 4, 100033.

[17] Adrian Cho (13 July 2012). “Higgs Boson Makes Its Debut After Decades-Long Search”. Science. 337

[18] Barton, A., Borissov, G., Bouhova-Thacker, E., Chilingarov, A., Davidson, R., de Mora, L., … & Walder, J. (2012). Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC. Physics Letters B, 716(1), 1-29.

[19] Higgs, P. W. (1964). Broken symmetries and the masses of gauge bosons. Physical Review Letters, 13(16), 508.

[20] Draper, P., Meade, P., Reece, M., & Shih, D. (2012). Implications of a 125 GeV Higgs boson for the MSSM and low-scale supersymmetry breaking. Physical Review D, 85(9), 095007.

[21] Witten, E. (1995). String theory dynamics in various dimensions. Nuclear Physics B, 443(1-2), 85-126.

[22] Comsa, I. M., Firsching, M., & Fischbacher, T. (2019). SO (8) supergravity and the magic of machine learning. Journal of High Energy Physics, 2019(8), 1-57.

[23] Bobev, N., Fischbacher, T., Gautason, F. F., & Pilch, K. (2020). A cornucopia of AdS 5 vacua. Journal of High Energy Physics, 2020(7), 1-42.

[24] Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., … & Zheng, X. (2016). Tensorflow: A system for large-scale machine learning. In 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16) (pp. 265-283).

[25] Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., … & Zheng, X. (2016). Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467.

[26] Matsubara, T., Ishikawa, A., & Yaguchi, T. (2020). Deep energy-based modeling of discrete-time physics. Advances in Neural Information Processing Systems, 33.

[27] Chauvin, Y., & Rumelhart, D. E. (Eds.). (1995). Backpropagation: theory, architectures, and applications. Psychology press.

[28] Petrescu, R. V., Aversa, R., Apicella, A., & Petrescu, F. I. (2018). NASA Data used to discover eighth planet circling distant star. Journal of Aircraft and Spacecraft Technology, 2(1), 19-30.

[29] Shallue, C. J., & Vanderburg, A. (2018). Identifying exoplanets with deep learning: A five-planet resonant chain around kepler-80 and an eighth planet around kepler-90. The Astronomical Journal, 155(2), 94.

[30] Dattilo, A., Vanderburg, A., Shallue, C. J., Mayo, A. W., Berlind, P., Bieryla, A., … & Yu, L. (2019). Identifying exoplanets with deep learning. ii. two new super-earths uncovered by a neural network in k2 data. The Astronomical Journal, 157(5), 169.

[31] Bechtle, P., Belkner, S., Dercks, D., Hamer, M., Keller, T., Krämer, M., … & Tattersall, J. (2017). SCYNet: testing supersymmetric models at the LHC with neural networks. The European Physical Journal C, 77(10), 1-20.

[32] Brehmer, J., Cranmer, K., Louppe, G., & Pavez, J. (2018). Constraining effective field theories with machine learning. Physical review letters, 121(11), 111801.

[33] Carrasquilla, J. (2020). Machine learning for quantum matter. Advances in Physics: X, 5(1), 1797528.

[34] Castelvecchi, D. (2015). Artificial intelligence called in to tackle LHC data deluge. Nature News, 528(7580), 18.

[35] Sadowski, P. J., Whiteson, D., & Baldi, P. (2014). Searching for higgs boson decay modes with deep learning. Advances in Neural Information Processing Systems, 27, 2393-2401.

[36] Bapst, F., Bhimji, W., Calafiura, P., Gray, H., Lavrijsen, W., Linder, L., & Smith, A. (2020). A pattern recognition algorithm for quantum annealers. Computing and Software for Big Science, 4(1), 1-7.

[37] Saito, M., Calafiura, P., Gray, H., Lavrijsen, W., Linder, L., Okumura, Y., … & Terashi, K. (2020). Quantum annealing algorithms for track pattern recognition. In EPJ Web of Conferences (Vol. 245, p. 10006). EDP Sciences.

[38] Aaltonen, T., Amerio, S., Amidei, D., Anastassov, A., Annovi, A., Antos, J., … & Herndon, M. (2013). Direct measurement of the total decay width of the Top Quark. Physical review letters, 111(20), 202001.

[39] Chakraborty, A., Lim, S. H., Nojiri, M. M., & Takeuchi, M. (2020). Neural network-based top tagger with two-point energy correlations and geometry of soft emissions. Journal of High Energy Physics, 2020(7), 1-47.

[40] Mika, S., Ratsch, G., Weston, J., Scholkopf, B., & Mullers, K. R. (1999, August). Fisher discriminant analysis with kernels. In Neural networks for signal processing IX: Proceedings of the 1999 IEEE signal processing society workshop (cat. no. 98th8468) (pp. 41-48). Ieee

[41] Davletov, E. T., Tsyganok, V. V., Khlebnikov, V. A., Pershin, D. A., Shaykin, D. V., & Akimov, A. V. (2020). Machine learning for achieving Bose-Einstein condensation of thulium atoms. Physical Review A, 102(1), 011302.

[42] Liang, X., Zhang, H., Liu, S., Li, Y., & Zhang, Y. S. (2018). Generation of Bose-Einstein Condensates’ Ground State Through Machine Learning. Scientific reports, 8(1), 1-8.

[43] Barker, A. J., Style, H., Luksch, K., Sunami, S., Garrick, D., Hill, F., … & Bentine, E. (2020). Applying machine learning optimization methods to the production of a quantum gas. Machine Learning: Science and Technology, 1(1), 015007.

[44] Saito, H. (2020). Creation and Manipulation of Quantized Vortices in Bose–Einstein Condensates Using Reinforcement Learning. Journal of the Physical Society of Japan, 89(7), 074006.

[45] Pilati, S., & Pieri, P. (2019). Supervised machine learning of ultracold atoms with speckle disorder. Scientific reports, 9(1), 1-12.

[46] Wigley, P. B., Everitt, P. J., van den Hengel, A., Bastian, J. W., Sooriyabandara, M. A., McDonald, G. D., … & Hush, M. R. (2016). Fast machine-learning online optimization of ultra-cold-atom experiments. Scientific reports, 6(1), 1-6.

[47] Fluke, C. J., & Jacobs, C. (2020). Surveying the reach and maturity of machine learning and artificial intelligence in astronomy. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(2), e1349.

[48] Hezaveh, Y. D., Levasseur, L. P., & Marshall, P. J. (2017). Fast automated analysis of strong gravitational lenses with convolutional neural networks. Nature, 548(7669), 555-557.

[49] Pourrahmani, M., Nayyeri, H., & Cooray, A. (2018). LensFlow: A convolutional neural network in search of strong gravitational lenses. The Astrophysical Journal, 856(1), 68.

[50] Caldeira, J., Wu, W. K., Nord, B., Avestruz, C., Trivedi, S., & Story, K. T. (2019). DeepCMB: Lensing reconstruction of the cosmic microwave background with deep neural networks. Astronomy and Computing, 28, 100307.

[51] Bouman, K. L., Johnson, M. D., Zoran, D., Fish, V. L., Doeleman, S. S., & Freeman, W. T. (2016). Computational imaging for VLBI image reconstruction. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 913-922).

[52] Akiyama, K., Kuramochi, K., Ikeda, S., Fish, V. L., Tazaki, F., Honma, M., … & Zaizen, M. (2017). Imaging the Schwarzschild-radius-scale structure of M87 with the Event Horizon Telescope using sparse modeling. The Astrophysical Journal, 838(1), 1.

[53] Bouman, K. L. (2020). Portrait of a black hole: Here’s how the event horizon telescope team pieced together a now-famous image. IEEE Spectrum, 57(2), 22-29.

[54] Askar, A., Askar, A., Pasquato, M., & Giersz, M. (2019). Finding black holes with black boxes–using machine learning to identify globular clusters with black hole subsystems. Monthly Notices of the Royal Astronomical Society, 485(4), 5345-5362.

[55] van der Gucht, J., Davelaar, J., Hendriks, L., Porth, O., Olivares, H., Mizuno, Y., … & Falcke, H. (2020). Deep horizon: A machine learning network that recovers accreting black hole parameters. Astronomy & Astrophysics, 636, A94.

[56] Kim, E. J., & Brunner, R. J. (2016). Star-galaxy classification using deep convolutional neural networks. Monthly Notices of the Royal Astronomical Society, stw2672.

[57] Lovell, C. C., Acquaviva, V., Thomas, P. A., Iyer, K. G., Gawiser, E., & Wilkins, S. M. (2019). Learning the relationship between galaxies spectra and their star formation histories using convolutional neural networks and cosmological simulations. Monthly Notices of the Royal Astronomical Society, 490(4), 5503-5520.

[58] Huertas-Company, M., Guo, Y., Ginzburg, O., Lee, C. T., Mandelker, N., Metter, M., … & Zhang, H. (2020). Stellar masses of giant clumps in CANDELS and simulated galaxies using machine learning. Monthly Notices of the Royal Astronomical Society, 499(1), 814-835.

[59] Sadowski, P., Collado, J., Whiteson, D., & Baldi, P. (2015, August). Deep learning, dark knowledge, and dark matter. In NIPS 2014 Workshop on High-energy Physics and Machine Learning (pp. 81-87). PMLR.

[60] Brehmer, J., Mishra-Sharma, S., Hermans, J., Louppe, G., & Cranmer, K. (2019). Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning. The Astrophysical Journal, 886(1), 49.

[61] Ravanbakhsh, S., Oliva, J., Fromenteau, S., Price, L., Ho, S., Schneider, J., & Póczos, B. (2016, June). Estimating cosmological parameters from the dark matter distribution. In International Conference on Machine Learning (pp. 2407-2416). PMLR.

[62] Erdmann, M., Glombitza, J., & Walz, D. (2018). A deep learning-based reconstruction of cosmic ray-induced air showers. Astroparticle Physics, 97, 46-53.

[63] Huntingford, C., Jeffers, E. S., Bonsall, M. B., Christensen, H. M., Lees, T., & Yang, H. (2019). Machine learning and artificial intelligence to aid climate change research and preparedness. Environmental Research Letters, 14(12), 124007.

[64] Gardner, M. W., & Dorling, S. R. (1998). Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmospheric environment, 32(14-15), 2627-2636.

[65] Ye, Z., Yang, J., Zhong, N., Tu, X., Jia, J., & Wang, J. (2020). Tackling environmental challenges in pollution controls using artificial intelligence: a review. Science of the Total Environment, 699, 134279.

[66] Chen, R., Zhang, W., & Wang, X. (2020). Machine learning in tropical cyclone forecast modeling: A review. Atmosphere, 11(7), 676.

[67] Chen, R., Zhang, W., & Wang, X. (2020). Machine learning in tropical cyclone forecast modeling: A review. Atmosphere, 11(7), 676.

[68] Bedaque, P., Boehnlein, A., Cromaz, M., Diefenthaler, M., Elouadrhiri, L., Horn, T., … & Wang, X. N. (2021). AI for nuclear physics. The European Physical Journal A, 57(3), 1-27.

[69] Carleo, G., & Troyer, M. (2017). Solving the quantum many-body problem with artificial neural networks. Science, 355(6325), 602-606.

[70] Lagaris, I. E., Likas, A., & Fotiadis, D. I. (1997). Artificial neural network methods in quantum mechanics. Computer Physics Communications, 104(1-3), 1-14.

[71] Lasseri, R. D., Regnier, D., Ebran, J. P., & Penon, A. (2020). Taming nuclear complexity with a committee of multilayer neural networks. Physical review letters, 124(16), 162502.

[72] Kong, X., Zhou, L., Li, Z., Yang, Z., Qiu, B., Wu, X., … & Du, J. (2020). Artificial intelligence enhanced two-dimensional nanoscale nuclear magnetic resonance spectroscopy. npj Quantum Information, 6(1), 1-10.

[73] Hall, M. (2019). Artificial intelligence and nuclear medicine. Nuclear medicine communications, 40(1), 1.

[74] Uhrig, R. E., & Hines, J. (2005). Computational intelligence in nuclear engineering. Nuclear Engineering and Technology, 37(2), 127-138.

[75] Gernoth, K. A., Clark, J. W., Prater, J. S., & Bohr, H. (1993). Neural network models of nuclear systematics. Physics Letters B, 300(1-2), 1-7.

[76] Theije, T. D. (2021). Improving the electron identification of the ALICE detector using machine learning (Bachelor’s thesis).

[77] Burger, M., Ruthotto, L., & Osher, S. (2021). Connections between deep learning and partial differential equations. European Journal of Applied Mathematics, 32(3), 395-396.

[78] Granade, C. E., Ferrie, C., Wiebe, N., & Cory, D. G. (2012). Robust online Hamiltonian learning. New Journal of Physics, 14(10), 103013.

[79] Gao, J., Qiao, L. F., Jiao, Z. Q., Ma, Y. C., Hu, C. Q., Ren, R. J., … & Jin, X. M. (2018). Experimental machine learning of quantum states. Physical review letters, 120(24), 240501.

[80] Kiani, B. T. (2020). Quantum artificial intelligence: learning unitary transformations (Doctoral dissertation, Massachusetts Institute of Technology).

[81] Gomes, C. P., Selman, B., & Gregoire, J. M. (2019). Artificial intelligence for materials discovery. MRS Bulletin, 44(7), 538-544.

[82] Dimiduk, D. M., Holm, E. A., & Niezgoda, S. R. (2018). Perspectives on the impact of machine learning, deep learning, and artificial intelligence on materials, processes, and structures engineering. Integrating Materials and Manufacturing Innovation, 7(3), 157-172.

[83] Rupp, M., Tkatchenko, A., Müller, K. R., & Von Lilienfeld, O. A. (2012). Fast and accurate modeling of molecular atomization energies with machine learning. Physical review letters, 108(5), 058301.

[84] Melnikov, A. A., Nautrup, H. P., Krenn, M., Dunjko, V., Tiersch, M., Zeilinger, A., & Briegel, H. J. (2018). Active learning machine learns to create new quantum experiments. Proceedings of the National Academy of Sciences, 115(6), 1221-1226.

[85] Dral, P. O., Owens, A., Dral, A., & Csányi, G. (2020). Hierarchical machine learning of potential energy surfaces. The Journal of Chemical Physics, 152(20), 204110.

[86] Mishin, Y. (2021). Machine-learning interatomic potentials for materials science. Acta Materialia, 214, 116980.

[87] Dong, X. Y., Pollmann, F., & Zhang, X. F. (2019). Machine learning of quantum phase transitions. Physical Review B, 99(12), 121104.

[88] Palmieri, A. M., Kovlakov, E., Bianchi, F., Yudin, D., Straupe, S., Biamonte, J. D., & Kulik, S. (2020). Experimental neural network enhanced quantum tomography. npj Quantum Information, 6(1), 1-5.

[89] Hentschel, A., & Sanders, B. C. (2011). Efficient algorithm for optimizing adaptive quantum metrology processes. Physical review letters, 107(23), 233601.

[90] Detmold, W., Kanwar, G., Lamm, H., Wagman, M. L., & Warrington, N. C. (2021). Path integral contour deformations for observables in S U (N) gauge theory. Physical Review D, 103(9), 094517.

[91] Potok, T. E., Schuman, C., Young, S., Patton, R., Spedalieri, F., Liu, J., … & Chakma, G. (2018). A study of complex deep learning networks on high-performance, neuromorphic, and quantum computers. ACM Journal on Emerging Technologies in Computing Systems (JETC), 14(2), 1-21.

[92] Olivares-Sánchez, J., Casanova, J., Solano, E., & Lamata, L. (2020). Measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti quantum computer. Quantum Reports, 2(2), 293-304.

[93] Jones, N. (2013). Google and NASA snap up quantum computer. Nature News.

[94] Marques, M. R., Wolff, J., Steigemann, C., & Marques, M. A. (2019). Neural network force fields for simple metals and semiconductors: construction and application to the calculation of phonons and melting temperatures. Physical Chemistry Chemical Physics, 21(12), 6506-6516.

[95] Yang, K. C., Yang, C., Chao, P. Y., & Shih, P. H. (2013). Applying artificial neural network to predict semiconductor machine outliers. Mathematical Problems in Engineering, 2013.

[96] Natarajan, N. Investigations on drilling of micro holes using electrical discharge machining.

[97] Goodlin, B. E., Boning, D. S., Sawin, H. H., & Wise, B. M. (2003). Simultaneous fault detection and classification for semiconductor manufacturing tools. Journal of the Electrochemical Society, 150(12), G778.

[98] Yang, K. C., Huang, C. H., Yang, C., Chao, P. Y., & Shih, P. H. (2014, June). Using artificial neural back-propagation network model to detect the outliers in semiconductor manufacturing machines. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (pp. 240-249). Springer, Cham.

[99] Kossovsky, A. E. (2020). Johannes Kepler—The First Data Analyst. In The Birth of Science (pp. 23-24). Springer, Cham.

[100] Udrescu, S. M., & Tegmark, M. (2020). AI Feynman: A physics-inspired method for symbolic regression. Science Advances, 6(16), eaay2631.

[101] Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P. (2017). Geometric deep learning: going beyond euclidean data. IEEE Signal Processing Magazine, 34(4), 18-42.

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