AI Feynman: a Physics-Inspired Method for Symbolic Regression

27 May 2019

Papers citing "AI Feynman: a Physics-Inspired Method for Symbolic Regression"

26 / 126 papers shown

Title
From Kepler to Newton: Explainable AI for Science Zelong Li Jianchao Ji Yongfeng Zhang 26 19 0 24 Nov 2021
Symbolic Regression via Neural-Guided Genetic Programming Population Seeding T. Nathan Mundhenk Mikel Landajuela Ruben Glatt Claudio Santiago Daniel Faissol Brenden K. Petersen 38 85 0 29 Oct 2021
Chaos as an interpretable benchmark for forecasting and data-driven modelling W. Gilpin AI4TS 27 76 0 11 Oct 2021
Learning Division with Neural Arithmetic Logic Modules Bhumika Mistry K. Farrahi Jonathon S. Hare 25 0 0 11 Oct 2021
Kinematically consistent recurrent neural networks for learning inverse problems in wave propagation Wrik Mallik R. Jaiman J. Jelovica AI4CE 25 3 0 08 Oct 2021
Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning Ziming Liu Yunyue Chen Yuanqi Du Max Tegmark PINN AI4CE 40 22 0 28 Sep 2021
Understanding Spending Behavior: Recurrent Neural Network Explanation and Interpretation Charl Maree C. Omlin AI4TS 24 5 0 24 Sep 2021
Machine-learning hidden symmetries Ziming Liu Max Tegmark 48 52 0 20 Sep 2021
KO codes: Inventing Nonlinear Encoding and Decoding for Reliable Wireless Communication via Deep-learning Ashok Vardhan Makkuva Xiyang Liu Mohammad Vahid Jamali Hessam Mahdavifar Sewoong Oh Pramod Viswanath 24 34 0 29 Aug 2021
DySMHO: Data-Driven Discovery of Governing Equations for Dynamical Systems via Moving Horizon Optimization F. Lejarza M. Baldea AI4CE 27 38 0 30 Jul 2021
Contemporary Symbolic Regression Methods and their Relative Performance William La Cava Patryk Orzechowski Bogdan Burlacu Fabrício Olivetti de Francca M. Virgolin Ying Jin M. Kommenda J. Moore 56 251 0 29 Jul 2021
Machine learning of Kondo physics using variational autoencoders and symbolic regression Cole Miles Matthew R. Carbone Erica J. Sturm D. Lu A. Weichselbaum K. Barros R. Konik 8 8 0 16 Jul 2021
Generative and reproducible benchmarks for comprehensive evaluation of machine learning classifiers Patryk Orzechowski J. Moore ELM VLM 17 13 0 14 Jul 2021
SymbolicGPT: A Generative Transformer Model for Symbolic Regression Mojtaba Valipour Bowen You Maysum Panju A. Ghodsi 18 88 0 27 Jun 2021
Neural Symbolic Regression that Scales Luca Biggio Tommaso Bendinelli Alexander Neitz Aurelien Lucchi Giambattista Parascandolo 54 170 0 11 Jun 2021
Data vs. Physics: The Apparent Pareto Front of Physics-Informed Neural Networks Franz M. Rohrhofer S. Posch C. Gößnitzer Bernhard C. Geiger PINN 25 39 0 03 May 2021
Symmetry meets AI G. Barenboim J. Hirn V. Sanz 25 21 0 10 Mar 2021
Disentangling a Deep Learned Volume Formula J. Craven Vishnu Jejjala Arjun Kar 13 19 0 07 Dec 2020
Interpretability and Explainability: A Machine Learning Zoo Mini-tour Ricards Marcinkevics Julia E. Vogt XAI 28 119 0 03 Dec 2020
Symbolically Solving Partial Differential Equations using Deep Learning Maysum Panju Kourosh Parand A. Ghodsi 8 3 0 12 Nov 2020
Logic Guided Genetic Algorithms D. Ashok Joseph Scott S. J. Wetzel Maysum Panju Vijay Ganesh 21 12 0 21 Oct 2020
Convolutional-network models to predict wall-bounded turbulence from wall quantities L. Guastoni A. Güemes A. Ianiro S. Discetti P. Schlatter Hossein Azizpour R. Vinuesa 28 167 0 22 Jun 2020
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity S. Udrescu A. Tan Jiahai Feng Orisvaldo Neto Tailin Wu Max Tegmark 11 183 0 18 Jun 2020
Recurrent neural networks and Koopman-based frameworks for temporal predictions in a low-order model of turbulence Hamidreza Eivazi L. Guastoni P. Schlatter Hossein Azizpour Ricardo Vinuesa AI4CE 18 7 0 01 May 2020
Discovering Symmetry Invariants and Conserved Quantities by Interpreting Siamese Neural Networks S. J. Wetzel R. Melko Joseph Scott Maysum Panju Vijay Ganesh 17 64 0 09 Mar 2020
A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay L. Smith 208 1,020 0 26 Mar 2018