Verification and Validation for Trustworthy Scientific Machine Learning

21 February 2025

John D. Jakeman

Lorena A. Barba

J. Martins

Thomas O'Leary-Roseberry

AI4CE

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Papers citing "Verification and Validation for Trustworthy Scientific Machine Learning"

34 / 34 papers shown

Title
Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations N. McGreivy Ammar Hakim AI4CE 62 48 0 09 Jul 2024
Mixture of Experts Soften the Curse of Dimensionality in Operator Learning Anastasis Kratsios Takashi Furuya Jose Antonio Lara Benitez Matti Lassas Maarten V. de Hoop 60 13 0 13 Apr 2024
Evolving Scientific Discovery by Unifying Data and Background Knowledge with AI Hilbert Ryan Cory-Wright Cristina Cornelio S. Dash Bachir El Khadir L. Horesh 33 10 0 18 Aug 2023
Efficient PDE-Constrained optimization under high-dimensional uncertainty using derivative-informed neural operators Dingcheng Luo Thomas O'Leary-Roseberry Peng Chen Omar Ghattas AI4CE 41 16 0 31 May 2023
A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling Qizhi He M. Perego Amanda A. Howard George Karniadakis P. Stinis 37 18 0 26 Jan 2023
PDEBENCH: An Extensive Benchmark for Scientific Machine Learning M. Takamoto T. Praditia Raphael Leiteritz Dan MacKinlay Francesco Alesiani Dirk Pflüger Mathias Niepert AI4CE 53 219 0 13 Oct 2022
Neural and spectral operator surrogates: unified construction and expression rate bounds L. Herrmann Christoph Schwab Jakob Zech 71 11 0 11 Jul 2022
Physics-Informed Deep Neural Operator Networks S. Goswami Aniruddha Bora Yue Yu George Karniadakis PINN AI4CE 57 102 0 08 Jul 2022
Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning Thomas O'Leary-Roseberry Peng Chen Umberto Villa Omar Ghattas AI4CE 46 40 0 21 Jun 2022
Toward a Taxonomy of Trust for Probabilistic Machine Learning Tamara Broderick Andrew Gelman Rachael Meager Anna L. Smith Tian Zheng 52 9 0 05 Dec 2021
Fast PDE-constrained optimization via self-supervised operator learning Sizhuang He Mohamed Aziz Bhouri P. Perdikaris 69 28 0 25 Oct 2021
Machine learning moment closure models for the radiative transfer equation II: enforcing global hyperbolicity in gradient based closures Juntao Huang Yingda Cheng Andrew J. Christlieb L. Roberts W. Yong AI4CE 36 18 0 30 May 2021
Learning to Optimize: A Primer and A Benchmark Tianlong Chen Xiaohan Chen Wuyang Chen Howard Heaton Jialin Liu Zhangyang Wang W. Yin 130 231 0 23 Mar 2021
POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition S. Fresca Andrea Manzoni AI4CE 48 213 0 28 Jan 2021
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges Christoph Molnar Giuseppe Casalicchio B. Bischl AI4TS AI4CE 64 400 0 19 Oct 2020
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 421 2,355 0 18 Oct 2020
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI Alon Jacovi Ana Marasović Tim Miller Yoav Goldberg 281 436 0 15 Oct 2020
On the representation and learning of monotone triangular transport maps Ricardo Baptista Youssef Marzouk O. Zahm 48 48 0 22 Sep 2020
MFNets: Data efficient all-at-once learning of multifidelity surrogates as directed networks of information sources Alex Gorodetsky J. Jakeman Gianluca Geraci AI4CE 50 24 0 03 Aug 2020
Conditional Sampling with Monotone GANs: from Generative Models to Likelihood-Free Inference Ricardo Baptista Bamdad Hosseini Nikola B. Kovachki Youssef Marzouk OT GAN 54 24 0 11 Jun 2020
Model Reduction and Neural Networks for Parametric PDEs K. Bhattacharya Bamdad Hosseini Nikola B. Kovachki Andrew M. Stuart 158 325 0 07 May 2020
The relationship between trust in AI and trustworthy machine learning technologies Ehsan Toreini Mhairi Aitken Kovila P. L. Coopamootoo Karen Elliott Carlos Vladimiro Gonzalez Zelaya Aad van Moorsel FaML 51 256 0 27 Nov 2019
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators Lu Lu Pengzhan Jin George Karniadakis 167 2,082 0 08 Oct 2019
Machine Learning Testing: Survey, Landscapes and Horizons Jie M. Zhang Mark Harman Lei Ma Yang Liu VLM AILaw 64 744 0 19 Jun 2019
Deep Learning of Preconditioners for Conjugate Gradient Solvers in Urban Water Related Problems Johannes Sappl L. Seiler M. Harders W. Rauch AI4CE 30 25 0 17 Jun 2019
Deep Gaussian Processes for Multi-fidelity Modeling Kurt Cutajar Mark Pullin Andreas C. Damianou Neil D. Lawrence Javier I. González AI4CE 41 110 0 18 Mar 2019
Rates of Convergence for Sparse Variational Gaussian Process Regression David R. Burt C. Rasmussen Mark van der Wilk 47 152 0 08 Mar 2019
The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems E. Weinan Ting Yu 103 1,373 0 30 Sep 2017
DGM: A deep learning algorithm for solving partial differential equations Justin A. Sirignano K. Spiliopoulos AI4CE 69 2,048 0 24 Aug 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 354 3,742 0 28 Feb 2017
Optimal weighted least-squares methods A. Cohen G. Migliorati 34 197 0 01 Aug 2016
Optimization Methods for Large-Scale Machine Learning Léon Bottou Frank E. Curtis J. Nocedal 177 3,198 0 15 Jun 2016
The Mythos of Model Interpretability Zachary Chase Lipton FaML 132 3,672 0 10 Jun 2016
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization Yann N. Dauphin Razvan Pascanu Çağlar Gülçehre Kyunghyun Cho Surya Ganguli Yoshua Bengio ODL 111 1,380 0 10 Jun 2014