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[1] Jure Leskovec,et al. GNNExplainer: Generating Explanations for Graph Neural Networks , 2019, NeurIPS.
[2] Frank Noé,et al. Machine Learning of Coarse-Grained Molecular Dynamics Force Fields , 2018, ACS central science.
[3] Hod Lipson,et al. Distilling Free-Form Natural Laws from Experimental Data , 2009, Science.
[4] Erik J Bekkers. B-Spline CNNs on Lie Groups , 2020, ICLR.
[5] Samuel S. Schoenholz,et al. Neural Message Passing for Quantum Chemistry , 2017, ICML.
[6] Yang Liu,et al. Deep learning of physical laws from scarce data , 2020, ArXiv.
[7] Shirley Ho,et al. Learning Symbolic Physics with Graph Networks , 2019, ArXiv.
[8] Joel Nothman,et al. SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python , 2019, ArXiv.
[9] John D. Hunter,et al. Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.
[10] S. Brunton,et al. Discovering governing equations from data by sparse identification of nonlinear dynamical systems , 2015, Proceedings of the National Academy of Sciences.
[11] Ah Chung Tsoi,et al. The Graph Neural Network Model , 2009, IEEE Transactions on Neural Networks.
[12] Pierre Vandergheynst,et al. Geometric Deep Learning: Going beyond Euclidean data , 2016, IEEE Signal Process. Mag..
[13] Omer San,et al. Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensor observation data , 2019 .
[14] Bryan C. Daniels,et al. Automated adaptive inference of phenomenological dynamical models , 2015, Nature Communications.
[15] Kenneth Chiu,et al. Prioritized grammar enumeration: symbolic regression by dynamic programming , 2013, GECCO '13.
[16] et al.,et al. Jupyter Notebooks - a publishing format for reproducible computational workflows , 2016, ELPUB.
[17] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[18] Miles Cranmer,et al. Lagrangian Neural Networks , 2020, ICLR 2020.
[19] Michael Burke,et al. Physics-as-Inverse-Graphics: Joint Unsupervised Learning of Objects and Physics from Video , 2019, ArXiv.
[20] Wes McKinney,et al. Data Structures for Statistical Computing in Python , 2010, SciPy.
[21] Michael Carbin,et al. The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks , 2018, ICLR.
[22] S. Levine,et al. Reasoning About Physical Interactions with Object-Centric Models , 2018 .
[23] Jason Yosinski,et al. Hamiltonian Neural Networks , 2019, NeurIPS.
[24] Razvan Pascanu,et al. On the Number of Linear Regions of Deep Neural Networks , 2014, NIPS.
[25] Ken-ichi Kawarabayashi,et al. What Can Neural Networks Reason About? , 2019, ICLR.
[26] Joshua B. Tenenbaum,et al. A Compositional Object-Based Approach to Learning Physical Dynamics , 2016, ICLR.
[27] Jure Leskovec,et al. Learning to Simulate Complex Physics with Graph Networks , 2020, ICML.
[28] Gaël Varoquaux,et al. The NumPy Array: A Structure for Efficient Numerical Computation , 2011, Computing in Science & Engineering.
[29] Marta Sales-Pardo,et al. A Bayesian machine scientist to aid in the solution of challenging scientific problems , 2020, Science Advances.
[30] Razvan Pascanu,et al. Relational inductive biases, deep learning, and graph networks , 2018, ArXiv.
[31] Gary Marcus,et al. The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence , 2020, ArXiv.
[32] Natalia Gimelshein,et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.
[33] Liping Wang,et al. Data-driven discovery of free-form governing differential equations , 2019, ArXiv.
[34] Andrew Gordon Wilson,et al. Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data , 2020, ICML.
[35] James P. Crutchfield,et al. Geometry from a Time Series , 1980 .
[36] Christoph H. Lampert,et al. Extrapolation and learning equations , 2016, ICLR.
[37] Andrea J. Liu,et al. A structural approach to relaxation in glassy liquids , 2015, Nature Physics.
[38] R. B. Barreiro,et al. Planck 2018 results , 2018, Astronomy & Astrophysics.
[39] Andrew Jaegle,et al. Hamiltonian Generative Networks , 2020, ICLR.
[40] Raia Hadsell,et al. Graph networks as learnable physics engines for inference and control , 2018, ICML.
[41] Razvan Pascanu,et al. Interaction Networks for Learning about Objects, Relations and Physics , 2016, NIPS.
[42] R. Zemel,et al. Neural Relational Inference for Interacting Systems , 2018, ICML.
[43] Kyle Cranmer,et al. Hamiltonian Graph Networks with ODE Integrators , 2019, ArXiv.
[44] Yuan Yu,et al. TensorFlow: A system for large-scale machine learning , 2016, OSDI.
[45] Christoph H. Lampert,et al. Learning Equations for Extrapolation and Control , 2018, ICML.
[46] Dario Izzo,et al. dcgp: Differentiable Cartesian Genetic Programming made easy , 2020, J. Open Source Softw..
[47] E. Wigner. The Unreasonable Effectiveness of Mathematics in the Natural Sciences (reprint) , 1960 .
[48] Edward J. Wollack,et al. First year Wilkinson Microwave Anisotropy Probe (WMAP) observations: Determination of cosmological parameters , 2003, astro-ph/0302209.
[49] Jure Leskovec,et al. GNN Explainer: A Tool for Post-hoc Explanation of Graph Neural Networks , 2019, ArXiv.
[50] Ali Ramadhan,et al. Universal Differential Equations for Scientific Machine Learning , 2020, ArXiv.
[51] Steven L. Brunton,et al. Data-driven discovery of partial differential equations , 2016, Science Advances.
[52] Jiajun Wu,et al. Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids , 2018, ICLR.
[53] Jan Eric Lenssen,et al. Fast Graph Representation Learning with PyTorch Geometric , 2019, ArXiv.
[54] Pushmeet Kohli,et al. Unveiling the predictive power of static structure in glassy systems , 2020 .
[55] G. Efstathiou,et al. The formation of dark halos in a universe dominated by cold dark matter , 1988 .
[56] Max Tegmark,et al. AI Feynman: A physics-inspired method for symbolic regression , 2019, Science Advances.
[57] Ross B. Girshick,et al. PHYRE: A New Benchmark for Physical Reasoning , 2019, NeurIPS.
[58] Anders Logg,et al. The Unreasonable Effectiveness of Mathematics in the Natural Sciences , 2004 .
[59] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[60] Daniel L. K. Yamins,et al. Flexible Neural Representation for Physics Prediction , 2018, NeurIPS.
[61] Marc Parizeau,et al. DEAP: evolutionary algorithms made easy , 2012, J. Mach. Learn. Res..
[62] Pierre Sens,et al. DeepMoD: Deep learning for model discovery in noisy data , 2019, J. Comput. Phys..
[63] Rishabh Singh,et al. Neural-Guided Symbolic Regression with Asymptotic Constraints , 2019 .