{"results":[{"id":"ss_e916f69e70a4321f21356f7ce360e380dd976a43","title":"Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next","authors":[{"name":"S. Cuomo"},{"name":"Vincenzo Schiano Di Cola"},{"name":"F. Giampaolo"},{"name":"G. Rozza"},{"name":"Maizar Raissi"},{"name":"F. Piccialli"}],"abstract":"Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like Partial Differential Equations (PDE), as a component of the neural network itself. PINNs are nowadays used to solve PDEs, fractional equations, integral-differential equations, and stochastic PDEs. This novel methodology has arisen as a multi-task learning framework in which a NN must fit observed data while reducing a PDE residual. This article provides a comprehensive review of the literature on PINNs: while the primary goal of the study was to characterize these networks and their related advantages and disadvantages. The review also attempts to incorporate publications on a broader range of collocation-based physics informed neural networks, which stars form the vanilla PINN, as well as many other variants, such as physics-constrained neural networks (PCNN), variational hp-VPINN, and conservative PINN (CPINN). The study indicates that most research has focused on customizing the PINN through different activation functions, gradient optimization techniques, neural network structures, and loss function structures. Despite the wide range of applications for which PINNs have been used, by demonstrating their ability to be more feasible in some contexts than classical numerical techniques like Finite Element Method (FEM), advancements are still possible, most notably theoretical issues that remain unresolved.","source":"Semantic Scholar","year":2022,"language":"en","subjects":["Computer Science","Mathematics","Physics"],"doi":"10.1007/s10915-022-01939-z","url":"https://www.semanticscholar.org/paper/e916f69e70a4321f21356f7ce360e380dd976a43","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10915-022-01939-z.pdf","is_open_access":true,"citations":2080,"published_at":"","score":96},{"id":"ss_53c9f3c34d8481adaf24df3b25581ccf1bc53f5c","title":"Physics-informed machine learning","authors":[{"name":"G. Karniadakis"},{"name":"I. Kevrekidis"},{"name":"Lu Lu"},{"name":"P. Perdikaris"},{"name":"Sifan Wang"},{"name":"Liu Yang"}],"abstract":"","source":"Semantic Scholar","year":2021,"language":"en","subjects":null,"doi":"10.1038/s42254-021-00314-5","url":"https://www.semanticscholar.org/paper/53c9f3c34d8481adaf24df3b25581ccf1bc53f5c","pdf_url":"https://www.osti.gov/biblio/2282016","is_open_access":true,"citations":5999,"published_at":"","score":95},{"id":"ss_49142e3e381c0dc7fee0049ea41d2ef02c0340d7","title":"Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning","authors":[{"name":"Viktor Makoviychuk"},{"name":"Lukasz Wawrzyniak"},{"name":"Yunrong Guo"},{"name":"Michelle Lu"},{"name":"Kier Storey"},{"name":"M. Macklin"},{"name":"David Hoeller"},{"name":"N. Rudin"},{"name":"Arthur Allshire"},{"name":"Ankur Handa"},{"name":"Gavriel State"}],"abstract":"Isaac Gym offers a high performance learning platform to train policies for wide variety of robotics tasks directly on GPU. Both physics simulation and the neural network policy training reside on GPU and communicate by directly passing data from physics buffers to PyTorch tensors without ever going through any CPU bottlenecks. This leads to blazing fast training times for complex robotics tasks on a single GPU with 2-3 orders of magnitude improvements compared to conventional RL training that uses a CPU based simulator and GPU for neural networks. We host the results and videos at \\url{https://sites.google.com/view/isaacgym-nvidia} and isaac gym can be downloaded at \\url{https://developer.nvidia.com/isaac-gym}.","source":"Semantic Scholar","year":2021,"language":"en","subjects":["Computer Science"],"url":"https://www.semanticscholar.org/paper/49142e3e381c0dc7fee0049ea41d2ef02c0340d7","is_open_access":true,"citations":1635,"published_at":"","score":95},{"id":"ss_d86084808994ac54ef4840ae65295f3c0ec4decd","title":"Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations","authors":[{"name":"M. Raissi"},{"name":"P. Perdikaris"},{"name":"G. Karniadakis"}],"abstract":"Abstract We introduce physics-informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations. In this work, we present our developments in the context of solving two main classes of problems: data-driven solution and data-driven discovery of partial differential equations. Depending on the nature and arrangement of the available data, we devise two distinct types of algorithms, namely continuous time and discrete time models. The first type of models forms a new family of data-efficient spatio-temporal function approximators, while the latter type allows the use of arbitrarily accurate implicit Runge–Kutta time stepping schemes with unlimited number of stages. The effectiveness of the proposed framework is demonstrated through a collection of classical problems in fluids, quantum mechanics, reaction–diffusion systems, and the propagation of nonlinear shallow-water waves.","source":"Semantic Scholar","year":2019,"language":"en","subjects":["Computer Science"],"doi":"10.1016/J.JCP.2018.10.045","url":"https://www.semanticscholar.org/paper/d86084808994ac54ef4840ae65295f3c0ec4decd","pdf_url":"http://manuscript.elsevier.com/S0021999118307125/pdf/S0021999118307125.pdf","is_open_access":true,"citations":15711,"published_at":"","score":93},{"id":"ss_7d15851cde781f3cc7f147c40e0c019c36bed520","title":"Plasma Physics via Computer Simulation","authors":[{"name":"C. Birdsall"},{"name":"A. Langdon"}],"abstract":"PART 1: PRIMER Why attempting to do plasma physics via computer simulation using particles makes good sense Overall view of a one dimensional electrostatic program A one dimensional electrostatic program ES1 Introduction to the numerical methods used Projects for ES1 A 1d electromagnetic program EM1 Projects for EM1 PART 2: THEORY Effects of the spatial grid Effects of the finitw time ste Energy-conserving simulation models Multipole models Kinetic theory for fluctuations and noise collisions Kinetic properties: theory, experience and heuristic estimates PART 3: PRACTICE Electrostatic programs in two and three dimensions Electromagnetic programs in two and three dimensions Particle loading, injection boudary conditions and external circuit PART 4: APPENDICES","source":"Semantic Scholar","year":2018,"language":"en","subjects":["Physics"],"doi":"10.1201/9781315275048","url":"https://www.semanticscholar.org/paper/7d15851cde781f3cc7f147c40e0c019c36bed520","is_open_access":true,"citations":5003,"published_at":"","score":92},{"id":"ss_2be523c24af8e385346e90746dfe93bdfbf22541","title":"Theory and Experiment in Gravitational Physics","authors":[{"name":"C. Will"}],"abstract":"New technological advances have made it feasible to conduct measurements with precision levels which are suitable for experimental tests of the theory of general relativity. This book has been designed to fill a new need for a complete treatment of techniques for analyzing gravitation theory and experience. The Einstein equivalence principle and the foundations of gravitation theory are considered, taking into account the Dicke framework, basic criteria for the viability of a gravitation theory, experimental tests of the Einstein equivalence principle, Schiff's conjecture, and a model theory devised by Lightman and Lee (1973). Gravitation as a geometric phenomenon is considered along with the parametrized post-Newtonian formalism, the classical tests, tests of the strong equivalence principle, gravitational radiation as a tool for testing relativistic gravity, the binary pulsar, and cosmological tests.","source":"Semantic Scholar","year":2018,"language":"en","subjects":["Physics"],"doi":"10.1017/9781316338612","url":"https://www.semanticscholar.org/paper/2be523c24af8e385346e90746dfe93bdfbf22541","is_open_access":true,"citations":3432,"published_at":"","score":92},{"id":"ss_35efaaed4b24057bc535f1200a0c07cc06c5d048","title":"Course in Theoretical Physics","authors":[{"name":"L. Landau"},{"name":"E. Lifshitz"}],"abstract":"","source":"Semantic Scholar","year":2013,"language":"en","subjects":["Physics"],"doi":"10.1016/0891-3919(58)90200-6","url":"https://www.semanticscholar.org/paper/35efaaed4b24057bc535f1200a0c07cc06c5d048","is_open_access":true,"citations":7027,"published_at":"","score":87},{"id":"ss_b354ee518bfc1ac0d8ac447eece9edb69e92eae1","title":"MuJoCo: A physics engine for model-based control","authors":[{"name":"E. Todorov"},{"name":"Tom Erez"},{"name":"Yuval Tassa"}],"abstract":"","source":"Semantic Scholar","year":2012,"language":"en","subjects":["Computer Science"],"doi":"10.1109/IROS.2012.6386109","url":"https://www.semanticscholar.org/paper/b354ee518bfc1ac0d8ac447eece9edb69e92eae1","is_open_access":true,"citations":6995,"published_at":"","score":86},{"id":"ss_654daa3cea17b46629b818ae157314617ceb7d90","title":"Review of Particle Physics","authors":[{"name":"K. Nakamura"},{"name":"K. Hagiwara"},{"name":"K. Hikasa"},{"name":"H. Murayama"},{"name":"M. 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Marciano"},{"name":"Alan D. Martin"},{"name":"A. Masoni"},{"name":"D. Milstead"},{"name":"R. Miquel"},{"name":"K. Mönig"},{"name":"M. Narain"},{"name":"P. Nason"},{"name":"S. Navas"},{"name":"P. Nevski"},{"name":"Y. Nir"},{"name":"K. Olive"},{"name":"L. Pape"},{"name":"C. Patrignani"},{"name":"J. Peacock"},{"name":"S. Petcov"},{"name":"A. Piepke"},{"name":"G. Punzi"},{"name":"A. Quadt"},{"name":"S. Raby"},{"name":"G. Raffelt"},{"name":"B. Ratcliff"},{"name":"P. Richardson"},{"name":"S. Roesler"},{"name":"S. Rolli"},{"name":"A. Romaniouk"},{"name":"L. Rosenberg"},{"name":"J. Rosner"},{"name":"C. Sachrajda"},{"name":"Y. Sakai"},{"name":"G. Salam"},{"name":"S. Sarkar"},{"name":"F. Sauli"},{"name":"O. Schneider"},{"name":"K. Scholberg"},{"name":"D. Scott"},{"name":"W. Seligman"},{"name":"M. Shaevitz"},{"name":"M. Silari"},{"name":"T. Sjöstrand"},{"name":"J. Smith"},{"name":"G. Smoot"},{"name":"S. Spanier"},{"name":"H. Spieler"},{"name":"A. Stahl"},{"name":"T. Stanev"},{"name":"S. Stone"},{"name":"T. Sumiyoshi"},{"name":"M. Syphers"},{"name":"J. Terning"},{"name":"M. Titov"},{"name":"N. P. Tkachenko"},{"name":"N. Tornqvist"},{"name":"D. Tovey"},{"name":"T. Trippe"},{"name":"G. Valencia"},{"name":"K. Bibber"},{"name":"G. Venanzoni"},{"name":"M. Vincter"},{"name":"P. Vogel"},{"name":"A. Vogt"},{"name":"W. Walkowiak"},{"name":"C. Walter"},{"name":"D. Ward"},{"name":"B. Webber"},{"name":"G. Weiglein"},{"name":"E. Weinberg"},{"name":"J. Wells"},{"name":"A. Wheeler"},{"name":"L. Wiencke"},{"name":"C. Wohl"},{"name":"L. Wolfenstein"},{"name":"J. Womersley"},{"name":"C. Woody"},{"name":"R. Workman"},{"name":"A. Yamamoto"},{"name":"W. Yao"},{"name":"O. Zenin"},{"name":"Jiayu Zhang"},{"name":"R. Zhu"},{"name":"P. Żyła"},{"name":"G. Harper"},{"name":"V. Lugovsky"},{"name":"P. Schaffner"}],"abstract":"The summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,717 new measurements from 869 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Most of the 120 reviews are updated, including many that are heavily revised. The is divided into two volumes. Volume 1 includes the Summary Tables and 97 review articles. Volume 2 consists of the Particle Listings and contains also 23 reviews that address specific aspects of the data presented in the Listings. The complete (both volumes) is published online on the website of the Particle Data Group () and in a journal. Volume 1 is available in print as the . A with the Summary Tables and essential tables, figures, and equations from selected review articles is available in print, as a web version optimized for use on phones, and as an Android app. The 2024 edition of the Review of Particle Physics should be cited as: S. Navas et al. (Particle Data Group), Phys. Rev. D 110, 030001 (2024)© 20242024","source":"Semantic Scholar","year":1996,"language":"en","subjects":["Physics"],"doi":"10.1016/J.PHYSLETB.2008.07.018","url":"https://www.semanticscholar.org/paper/654daa3cea17b46629b818ae157314617ceb7d90","pdf_url":"https://biblio.ugent.be/publication/685594/file/1130605.pdf","is_open_access":true,"citations":23619,"published_at":"","score":80},{"id":"ss_49cdf16489ee008649a00037cb22041a7bcd96ec","title":"CRC Handbook of Chemistry and Physics","authors":[{"name":"W. 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Furthermore, extensive information is provided on all program elements: subroutines and functions, switches and parameters, and particle and process data. This should allow the user to tailor the generation task to the topics of interest.","source":"Semantic Scholar","year":2006,"language":"en","subjects":["Physics"],"doi":"10.1088/1126-6708/2006/05/026","url":"https://www.semanticscholar.org/paper/1a3c2eb5d562d3d06d7e5c71a03f8ca3d52c919f","pdf_url":"https://arxiv.org/pdf/hep-ph/0108264","is_open_access":true,"citations":9817,"published_at":"","score":80},{"id":"ss_bf97653c065591cc23324b1ff5ddac020be67b0b","title":"Scaling Concepts in Polymer Physics","authors":[{"name":"P. 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Vansevivcius"},{"name":"Weiqiang Yang"},{"name":"W. Hellwing"},{"name":"Xin Ren"},{"name":"Yu-Min Hu"},{"name":"Yuejia Zhai"},{"name":"Abdul Malik Sultan"},{"name":"Adrienn Pataki"},{"name":"Alessandro Santoni"},{"name":"Aliya Batool"},{"name":"A. Wojnar"},{"name":"A. Tursunov"},{"name":"Avik De"},{"name":"Ayush Hazarika"},{"name":"Baojiu Li"},{"name":"Benjamin Bose"},{"name":"B. Mishra"},{"name":"B. Ahmedov"},{"name":"C. Sc'occola"},{"name":"Crescenzo Tortora"},{"name":"D. Kenworthy"},{"name":"Daniel E. Holz"},{"name":"David F. Mota"},{"name":"David S. Pereira"},{"name":"Devon Williams"},{"name":"D. Brout"},{"name":"Dong Ha Lee"},{"name":"Eduardo Guendelman"},{"name":"Edward Olex"},{"name":"Emanuelly Silva"},{"name":"E. Kahya"},{"name":"E. Mueller"},{"name":"F. Andrade-Oliveira"},{"name":"Feven Markos Hunde"},{"name":"F. R. Joaquim"},{"name":"F. Pacaud"},{"name":"F. Cyr-Racine"},{"name":"Pozo Nunez"},{"name":"F. G. R'acz"},{"name":"Gene Carlo Belinario"},{"name":"Geraint F. Lewis"},{"name":"G. D'alya"},{"name":"Giorgio Laverda"},{"name":"G. Risaliti"},{"name":"G. Franco-Abell'an"},{"name":"Hayden Zammit"},{"name":"H. Camilleri"},{"name":"H. Courtois"},{"name":"H. Moradpour"},{"name":"Igor de O. C. Pedreira"},{"name":"I. Lopes"},{"name":"I. Csabai"},{"name":"J. Rohlf"},{"name":"J. Bogdanoska"},{"name":"J. P'erez"},{"name":"Joan Bachs-Esteban"},{"name":"Joseph Sultana"},{"name":"J. Lesgourgues"},{"name":"Jun-Qian Jiang"},{"name":"Karem Penal'o Castillo"},{"name":"L. Heisenberg"},{"name":"Laxmipriya Pati"},{"name":"L. Koopmans"},{"name":"L. K. Duchaniya"},{"name":"L. Lombriser"},{"name":"Mar'ia P'erez Garrote"},{"name":"Mariano Dom'inguez"},{"name":"M. Samsonyan"},{"name":"Mark Pace"},{"name":"Martin Krvsvs'ak"},{"name":"M. C. Pookkillath"},{"name":"Matteo Peronaci"},{"name":"Matteo Piani"},{"name":"Matthildi Raftogianni"},{"name":"Meet J. Vyas"},{"name":"M. Michalopoulou"},{"name":"M. Gogberashvili"},{"name":"Michael Klasen"},{"name":"M. Cicoli"},{"name":"M. Quartin"},{"name":"Miguel Zumalac'arregui"},{"name":"M. S. Dimitrijevi'c"},{"name":"M. Dordevic"},{"name":"Mindaugas Karvciauskas"},{"name":"M. L. Delliou"},{"name":"N. Grimm"}],"abstract":"The standard model of cosmology has provided a good phenomenological description of a wide range of observations both at astrophysical and cosmological scales for several decades. This concordance model is constructed by a universal cosmological constant and supported by a matter sector described by the standard model of particle physics and a cold dark matter contribution, as well as very early-time inflationary physics, and underpinned by gravitation through general relativity. There have always been open questions about the soundness of the foundations of the standard model. However, recent years have shown that there may also be questions from the observational sector with the emergence of differences between certain cosmological probes. In this White Paper, we identify the key objectives that need to be addressed over the coming decade together with the core science projects that aim to meet these challenges. These discordances primarily rest on the divergence in the measurement of core cosmological parameters with varying levels of statistical confidence. These possible statistical tensions may be partially accounted for by systematics in various measurements or cosmological probes but there is also a growing indication of potential new physics beyond the standard model. After reviewing the principal probes used in the measurement of cosmological parameters, as well as potential systematics, we discuss the most promising array of potential new physics that may be observable in upcoming surveys. We also discuss the growing set of novel data analysis approaches that go beyond traditional methods to test physical models. [Abridged]","source":"Semantic Scholar","year":2025,"language":"en","subjects":["Physics"],"doi":"10.1016/j.dark.2025.101965","url":"https://www.semanticscholar.org/paper/d23a9c30f0753efaf1fd7bd160d9fb517822ac1b","is_open_access":true,"citations":208,"published_at":"","score":75.24000000000001}],"total":4999165,"page":1,"page_size":20,"sources":["CrossRef","arXiv","DOAJ","Semantic Scholar"],"query":"Physics"}