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On the definition and importance of interpretability in scientific machine learning

16 May 2025

Papers citing "On the definition and importance of interpretability in scientific machine learning"

24 / 24 papers shown

Title
Physics-informed sensor coverage through structure preserving machine learning Benjamin D. Shaffer Brooks Kinch Joseph Klobusicky M. Ani Hsieh Nathaniel Trask AI4CE 12 1 0 12 Sep 2025
Learning Adaptive Hydrodynamic Models Using Neural ODEs in Complex Conditions Cong Wang Aoming Liang Fei Han Xinyu Zeng Zhibin Li Dixia Fan Jens Kober AI4CE 129 2 0 01 Oct 2024
Neural Green's Operators for Parametric Partial Differential Equations Hugo Melchers Joost Prins Michael Abdelmalik 265 6 0 04 Jun 2024
Discovering an interpretable mathematical expression for a full wind-turbine wake with artificial intelligence enhanced symbolic regression Ding Wang Yuntian Chen Shiyi Chen 147 4 0 02 Jun 2024
Mechanistic Interpretability for AI Safety -- A Review Leonard Bereska E. Gavves AI4CE 187 201 0 22 Apr 2024
GINN-LP: A Growing Interpretable Neural Network for Discovering Multivariate Laurent Polynomial Equations Nisal Ranasinghe Damith A. Senanayake Sachith Seneviratne Malin Premaratne Saman K. Halgamuge 163 4 0 18 Dec 2023
Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics J. Fuhg Reese E. Jones N. Bouklas AI4CE 115 31 0 05 Oct 2023
LNO: Laplace Neural Operator for Solving Differential Equations Qianying Cao S. Goswami George Karniadakis 157 52 0 19 Mar 2023
Discovering interpretable Lagrangian of dynamical systems from data Tapas Tripura S. Chakraborty 92 4 0 09 Feb 2023
Interpretable Scientific Discovery with Symbolic Regression: A Review N. Makke Sanjay Chawla 186 147 0 20 Nov 2022
Rediscovering orbital mechanics with machine learning Pablo Lemos N. Jeffrey M. Cranmer S. Ho Peter W. Battaglia PINN AI4CE 109 106 0 04 Feb 2022
A toolkit for data-driven discovery of governing equations in high-noise regimes Charles B. Delahunt J. Nathan Kutz 147 23 0 08 Nov 2021
Data-driven Tissue Mechanics with Polyconvex Neural Ordinary Differential Equations Vahidullah Tac F. Sahli Costabal A. B. Tepole AI4CE 200 75 0 03 Oct 2021
Discovering Sparse Interpretable Dynamics from Partial Observations Peter Y. Lu Joan Ariño Bernad Marin Soljacic AI4CE 103 30 0 22 Jul 2021
Symbolic regression for scientific discovery: an application to wind speed forecasting Ismail Alaoui Abdellaoui S. Mehrkanoon 90 22 0 21 Feb 2021
Fourier Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 795 2,771 0 18 Oct 2020
A Bayesian machine scientist to aid in the solution of challenging scientific problems Roger Guimerà I. Reichardt Antoni Aguilar-Mogas F. Massucci Manuel Miranda J. Pallarés Marta Sales-Pardo AI4CE 86 126 0 25 Apr 2020
AI Feynman: a Physics-Inspired Method for Symbolic Regression S. Udrescu Max Tegmark 323 965 0 27 May 2019
Interpretable machine learning: definitions, methods, and applications W. James Murdoch Chandan Singh Karl Kumbier R. Abbasi-Asl Bin Yu XAI HAI 249 1,511 0 14 Jan 2019
Explaining Explanations: An Overview of Interpretability of Machine Learning Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal XAI 420 1,927 0 31 May 2018
The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches Md. Zahangir Alom T. Taha C. Yakopcic Stefan Westberg P. Sidike Mst Shamima Nasrin B. Van Essen A. Awwal V. Asari VLM 207 905 0 03 Mar 2018
Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations M. Raissi PINN AI4CE 161 785 0 20 Jan 2018
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 551 4,004 0 28 Feb 2017
European Union regulations on algorithmic decision-making and a "right to explanation" B. Goodman Seth Flaxman FaML AILaw 185 1,965 0 28 Jun 2016