Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

10 March 2020

Papers citing "Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems"

36 / 36 papers shown

Title
Knowledge Guided Encoder-Decoder Framework: Integrating Multiple Physical Models for Agricultural Ecosystem Modeling Qi Cheng Licheng Liu Yao Zhang Mu Hong Shiyuan Luo Zhenong Jin Yiqun Xie Xiaowei Jia 31 0 0 05 May 2025
Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction Melkamu Mersha Khang Lam Joseph Wood Ali AlShami Jugal Kalita XAI AI4TS 67 28 0 30 Aug 2024
RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate Models Pritthijit Nath Henry Moss Emily Shuckburgh Mark Webb AI4Cl AI4CE 34 0 0 28 Aug 2024
Hybrid Modeling Design Patterns Maja Rudolph Stefan Kurz Barbara Rakitsch AI4CE 28 8 0 29 Dec 2023
Learning-Initialized Trajectory Planning in Unknown Environments Yicheng Chen Jinjie Li Wenyuan Qin Yongzhao Hua Xiwang Dong Qingdong Li 21 0 0 19 Sep 2023
Explainable Predictive Maintenance Sepideh Pashami Sławomir Nowaczyk Yuantao Fan Jakub Jakubowski Nuno Paiva ... Bruno Veloso M. Sayed-Mouchaweh L. Rajaoarisoa Grzegorz J. Nalepa João Gama 32 8 0 08 Jun 2023
Pseudo-Hamiltonian neural networks for learning partial differential equations Sølve Eidnes K. Lye 18 10 0 27 Apr 2023
Nonlinear MPC for Quadrotors in Close-Proximity Flight with Neural Network Downwash Prediction Jinjie Li Liang Han Haoyang Yu Yuheng Lin Qingdong Li Z. Ren 26 8 0 16 Apr 2023
Neural DAEs: Constrained neural networks Tue Boesen E. Haber Uri M. Ascher 31 3 0 25 Nov 2022
Physics Informed Machine Learning for Chemistry Tabulation A. Salunkhe Dwyer Deighan P. DesJardin V. Chandola 10 6 0 06 Nov 2022
Tunable Complexity Benchmarks for Evaluating Physics-Informed Neural Networks on Coupled Ordinary Differential Equations Alexander New B. Eng A. Timm A. Gearhart 17 4 0 14 Oct 2022
NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators Zongren Zou Xuhui Meng Apostolos F. Psaros George Karniadakis AI4CE 25 35 0 25 Aug 2022
Physics-informed neural networks for PDE-constrained optimization and control Jostein Barry-Straume A. Sarshar Andrey A. Popov Adrian Sandu PINN AI4CE 27 14 0 06 May 2022
Supplementation of deep neural networks with simplified physics-based features to increase model prediction accuracy Nicholus R. Clinkinbeard Nicole N. Hashemi PINN AI4CE 33 0 0 14 Apr 2022
A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic Barbara Z Cunha C. Droz A. Zine Stéphane Foulard M. Ichchou AI4CE 29 84 0 13 Apr 2022
Trust in AI: Interpretability is not necessary or sufficient, while black-box interaction is necessary and sufficient Max W. Shen 25 18 0 10 Feb 2022
Incorporating Sum Constraints into Multitask Gaussian Processes Philipp Pilar Carl Jidling Thomas B. Schon Niklas Wahlström TPM 15 3 0 03 Feb 2022
Physics-enhanced deep surrogates for partial differential equations R. Pestourie Youssef Mroueh Chris Rackauckas Payel Das Steven G. Johnson PINN AI4CE 25 15 0 10 Nov 2021
Failure-averse Active Learning for Physics-constrained Systems Cheolhei Lee Xing Wang Jianguo Wu Xiaowei Yue AI4CE 19 7 0 27 Oct 2021
Physics-constrained Deep Learning for Robust Inverse ECG Modeling Jianxin Xie B. Yao 18 21 0 26 Jul 2021
Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations Benjamin Moseley Andrew Markham T. Nissen‐Meyer PINN 45 209 0 16 Jul 2021
Physics guided machine learning using simplified theories Suraj Pawar Omer San Burak Aksoylu Adil Rasheed T. Kvamsdal PINN AI4CE 97 106 0 18 Dec 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 205 2,282 0 18 Oct 2020
MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework C. Jiang S. Esmaeilzadeh Kamyar Azizzadenesheli K. Kashinath Mustafa A. Mustafa H. Tchelepi P. Marcus P. Prabhat Anima Anandkumar AI4CE 187 141 0 01 May 2020
Inverse Problems, Deep Learning, and Symmetry Breaking Kshitij Tayal Chieh-Hsin Lai Vipin Kumar Ju Sun AI4CE 72 15 0 20 Mar 2020
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 180 758 0 13 Mar 2020
Physics-Guided Deep Neural Networks for Power Flow Analysis Xinyue Hu Haoji Hu Saurabh Verma Zhi-Li Zhang 123 123 0 31 Jan 2020
Symplectic Recurrent Neural Networks Zhengdao Chen Jianyu Zhang Martín Arjovsky Léon Bottou 146 219 0 29 Sep 2019
Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods Ferdinando Fioretto Terrence W.K. Mak Pascal Van Hentenryck AI4CE 78 199 0 19 Sep 2019
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics C. Wehmeyer Frank Noé AI4CE BDL 109 355 0 30 Oct 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 270 5,660 0 05 Dec 2016
A Compositional Object-Based Approach to Learning Physical Dynamics Michael Chang T. Ullman Antonio Torralba J. Tenenbaum AI4CE OCL 241 438 0 01 Dec 2016
Interaction Networks for Learning about Objects, Relations and Physics Peter W. Battaglia Razvan Pascanu Matthew Lai Danilo Jimenez Rezende Koray Kavukcuoglu AI4CE OCL PINN GNN 278 1,400 0 01 Dec 2016
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 247 3,236 0 24 Nov 2016
Variational Koopman models: slow collective variables and molecular kinetics from short off-equilibrium simulations Hao Wu Feliks Nuske Fabian Paul Stefan Klus P. Koltai Frank Noé 104 126 0 20 Oct 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015