Paris Perdikaris
University of Pennsylvania
H-index: 45
North America-United States
Top articles of Paris Perdikaris
Title | Journal | Author(s) | Publication Date |
|---|---|---|---|
PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks | arXiv preprint arXiv:2402.00326 | Sifan Wang Bowen Li Yuhan Chen Paris Perdikaris | 2024/2/1 |
Composite Bayesian Optimization In Function Spaces Using NEON--Neural Epistemic Operator Networks | arXiv preprint arXiv:2404.03099 | Leonardo Ferreira Guilhoto Paris Perdikaris | 2024/4/3 |
Guided Autoregressive Diffusion Models with Applications to PDE Simulation | Federico Bergamin Cristiana Diaconu Aliaksandra Shysheya Paris Perdikaris José Miguel Hernández-Lobato | 2024/3/3 | |
Respecting causality for training physics-informed neural networks | Computer Methods in Applied Mechanics and Engineering | Sifan Wang Shyam Sankaran Paris Perdikaris | 2024/3/1 |
Learning Only on Boundaries: A Physics-Informed Neural Operator for Solving Parametric Partial Differential Equations in Complex Geometries | Neural Computation | Zhiwei Fang Sifan Wang Paris Perdikaris | 2024/2/16 |
Pde-refiner: Achieving accurate long rollouts with neural pde solvers | Advances in Neural Information Processing Systems | Phillip Lippe Bas Veeling Paris Perdikaris Richard Turner Johannes Brandstetter | 2024/2/13 |
Variational autoencoding neural operators | arXiv preprint arXiv:2302.10351 | Jacob H Seidman Georgios Kissas George J Pappas Paris Perdikaris | 2023/2/20 |
Computer systems and methods for learning operators | 2023/7/6 | ||
Long-time integration of parametric evolution equations with physics-informed deeponets | Journal of Computational Physics | Sifan Wang Paris Perdikaris | 2023/2/15 |
A dive into spectral inference networks: improved algorithms for self-supervised learning of continuous spectral representations | Applied Mathematics and Mechanics | J Wu SF Wang P Perdikaris | 2023/7 |
Scalable Bayesian optimization with randomized prior networks | Computer Methods in Applied Mechanics and Engineering | Mohamed Aziz Bhouri Michael Joly Robert Yu Soumalya Sarkar Paris Perdikaris | 2023/12/1 |
Scalable bayesian optimization with high-dimensional outputs using randomized prior networks | arXiv preprint arXiv:2302.07260 | Mohamed Aziz Bhouri Michael Joly Robert Yu Soumalya Sarkar Paris Perdikaris | 2023/2/14 |
Semi-supervised invertible neural operators for Bayesian inverse problems | Computational Mechanics | Sebastian Kaltenbach Paris Perdikaris Phaedon-Stelios Koutsourelakis | 2023/9 |
Ppdonet: Deep operator networks for fast prediction of steady-state solutions in disk–planet systems | The Astrophysical Journal Letters | Shunyuan Mao Ruobing Dong Lu Lu Kwang Moo Yi Sifan Wang | 2023/6/16 |
Accelerated design of architected materials with multifidelity Bayesian optimization | Journal of Engineering Mechanics | Chengyang Mo Paris Perdikaris Jordan R Raney | 2023/6/1 |
An expert's guide to training physics-informed neural networks | arXiv preprint arXiv:2308.08468 | Sifan Wang Shyam Sankaran Hanwen Wang Paris Perdikaris | 2023/8/16 |
Ensemble learning for physics informed neural networks: A gradient boosting approach | arXiv preprint arXiv:2302.13143 | Zhiwei Fang Sifan Wang Paris Perdikaris | 2023/2/25 |
Modeling accurate long rollouts with temporal neural PDE solvers | Phillip Lippe Bastiaan S Veeling Paris Perdikaris Richard E Turner Johannes Brandstetter | 2023/7/9 | |
Gaussian processes meet NeuralODEs: a Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data | Philosophical Transactions of the Royal Society A | Mohamed Aziz Bhouri Paris Perdikaris | 2022/8/8 |
Feasibility of Vascular Parameter Estimation for Assessing Hypertensive Pregnancy Disorders | Journal of Biomechanical Engineering | Georgios Kissas Eileen Hwuang Elizabeth W Thompson Nadav Schwartz John A Detre | 2022/12/1 |
Co-Authors
