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Artificial intelligence (AI) has been increasingly applied in scientific activities for decades; however, it is still far from an insightful and trustworthy collaborator in the scientific process.
Differentiable Physics-Informed Graph Networks
Seo, S.; and Liu, Y. 2019 · 1902
Earlier work this paper cites.
Learning Symbolic Physics with Graph Networks
Cranmer, M. D.; Xu, R.; Battaglia, P.; and Ho, S. 2019 · 1909
Earlier work this paper cites.
An Application of Algebraic Topology to Numerical Analysis: On the Existence of a Solution to the Network Problem
Roth, J. P. 1955 · 1955
Earlier work this paper cites.
Diakoptics: The Piecewise Solution of Large-Scale Systems , volume 2
Kron, G. 1963 · 1963
Earlier work this paper cites.
The Algebraic-Topological Basis for Network Analogies and the Vector Calculus
Branin, F. H. 1966 · 1966
Earlier work this paper cites.
Differential Forms in Algebraic Topology
Bott, R.; and Tu, L. W. 1982 · 1982
Earlier work this paper cites.
The Computer-Aided Discovery of Scientific Knowledge
Langley, P. 1998 · 1998
Earlier work this paper cites.
The Finite Volume, Finite Element, and Finite Difference Methods as Numerical Methods for Physical Field Problems
Mattiussi, C. 2000 · 2000
Earlier work this paper cites.
Algebraic Topology
Hatcher, A. 2001 · 2001
Earlier work this paper cites.
Operationally Meaningful Representations of Physical Systems in Neural Networks
Nautrup, H. P.; Metger, T.; Iten, R.; Jerbi, S.; Trenkwalder, L. M.; Wilming, H.; Briegel, H. J.; and Renner, R. 2020 · 2001
Earlier work this paper cites.
Learning to Simulate Complex Physics with Graph Networks
Sanchez-Gonzalez, A.; Godwin, J.; Pfaff, T.; Ying, R.; Leskovec, J.; and Battaglia, P. W. 2020 · 2002
Earlier work this paper cites.
Discrete Exterior Calculus
Hirani, A. N. 2003 · 2003
Earlier work this paper cites.
A Survey of Deep Learning for Scientific Discovery
Raghu, M.; and Schmidt, E. 2020 · 2003
Cited alongside, same era.
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Miyanawala, T. P.; and Jaiman, R. K. 2017 · 2008
Cited alongside, same era.
Distilling Free-Form Natural Laws from Experimental Data
Schmidt, M.; and Lipson, H. 2009 · 2009
Cited alongside, same era.
The Geometry of Physics: An Introduction
Frankel, T. 2011 · 2011
Cited alongside, same era.
The Mathematical Structure of Classical and Relativistic Physics: A General Classification Diagram
Tonti, E. 2013 · 2013
Cited alongside, same era.
Physics-Compatible Numerical Methods
Koren, B.; Abgrall, R.; Bochev, P.; Frank, J. E.; and Perot, B. 2014 · 2014
A High-Bias, Low-Variance Introduction to Machine Learning for Physicists
Mehta, P.; Bukov, M.; Wang, C. H.; Day, A. G. R.; Richardson, C.; Fisher, C. K.; and Schwab, D. J. 2019 · 2019
Later among the works it cites.
Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations
Raissi, M.; Perdikaris, P.; and Karniadakis, G. E. 2019 · 2019
Later among the works it cites.
Physics-Inspired Convolutional Neural Network for Solving Full-Wave Inverse Scattering Problems
Wei, Z.; and Chen, X. 2019 · 2019
Later among the works it cites.
Toward an AI Physicist for Unsupervised Learning
Wu, T.; and Tegmark, M. 2019 · 2019
Later among the works it cites.
Machine-Learning-Guided Directed Evolution for Protein Engineering
Yang, K. K.; Wu, Z.; and Arnold, F. H. 2019 · 2019
Later among the works it cites.
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Cited alongside, same era.
Mimetic Finite Difference Method
Lipnikov, K.; Manzini, G.; and Shashkov, M. 2014 · 2014
Cited alongside, same era.
Physics-Compatible Discretization Techniques on Single and Dual Grids, with Application to the Poisson Equation of Volume Forms
Palha, A.; Rebelo, P. P.; Hiemstra, R.; Kreeft, J.; and Gerritsma, M. 2014 · 2014
Cited alongside, same era.
Artificial Intelligence to Win the Nobel Prize and Beyond: Creating the Engine for Scientific Discovery
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Cited alongside, same era.
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Rudy, S. H.; Brunton, S. L.; Proctor, J. L.; and Kutz, J. N. 2017 · 2017
Cited alongside, same era.
Machine Learning for Molecular and Materials Science
Butler, K. T.; Davies, D. W.; Cartwright, H.; Isayev, O.; and Walsh, A. 2018 · 2018
Cited alongside, same era.
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Breen, P. G.; Foley, C. N.; Boekholt, T.; and Zwart, S. P. 2020 · 2020
Later among the works it cites.
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Daw, A.; Thomas, R. Q.; Carey, C. C.; Read, J. S.; Appling, A. P.; and Karpatne, A. 2020 · 2020
Later among the works it cites.
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Iten, R.; Metger, T.; Wilming, H.; Del Rio, L.; and Renner, R. 2020 · 2020
Later among the works it cites.
AI for Science
Stevens, R.; Taylor, V.; Nichols, J.; Maccabe, A. B.; Yelick, K.; and Brown, D. 2020 · 2020
Later among the works it cites.
AI Feynman: A Physics-Inspired Method for Symbolic Regression
Udrescu, S. M.; and Tegmark, M. 2020 · 2020
Later among the works it cites.
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Wang, R.; Kashinath, K.; Mustafa, M.; Albert, A.; and Yu, R. 2020 · 2020
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