Paris Perdikaris

research

∙ 08/24/2023

Learning Only On Boundaries: a Physics-Informed Neural operator for Solving Parametric Partial Differential Equations in Complex Geometries

Recently deep learning surrogates and neural operators have shown promis...

0 Zhiwei Fang, et al. ∙

research

∙ 08/16/2023

An Expert's Guide to Training Physics-informed Neural Networks

Physics-informed neural networks (PINNs) have been popularized as a deep...

0 Sifan Wang, et al. ∙

research

∙ 08/10/2023

PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers

Time-dependent partial differential equations (PDEs) are ubiquitous in s...

0 Phillip Lippe, et al. ∙

research

∙ 05/18/2023

PPDONet: Deep Operator Networks for Fast Prediction of Steady-State Solutions in Disk-Planet Systems

We develop a tool, which we name Protoplanetary Disk Operator Network (P...

0 Shunyuan Mao, et al. ∙

research

∙ 05/15/2023

Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior

Data-driven approaches achieve remarkable results for the modeling of co...

0 Thomas Beckers, et al. ∙

research

∙ 02/25/2023

Ensemble learning for Physics Informed Neural Networks: a Gradient Boosting approach

While the popularity of physics-informed neural networks (PINNs) is stea...

0 Zhiwei Fang, et al. ∙

research

∙ 02/20/2023

Variational Autoencoding Neural Operators

Unsupervised learning with functional data is an emerging paradigm of ma...

0 Jacob H. Seidman, et al. ∙

research

∙ 02/14/2023

Scalable Bayesian optimization with high-dimensional outputs using randomized prior networks

Several fundamental problems in science and engineering consist of globa...

0 Mohamed Aziz Bhouri, et al. ∙

research

∙ 10/03/2022

Random Weight Factorization Improves the Training of Continuous Neural Representations

Continuous neural representations have recently emerged as a powerful an...

0 Sifan Wang, et al. ∙

research

∙ 09/08/2022

Δ-PINNs: physics-informed neural networks on complex geometries

Physics-informed neural networks (PINNs) have demonstrated promise in so...

10 Francisco Sahli Costabal, et al. ∙

research

∙ 09/06/2022

Semi-supervised Invertible DeepONets for Bayesian Inverse Problems

Deep Operator Networks (DeepONets) offer a powerful, data-driven tool fo...

0 Sebastian Kaltenbach, et al. ∙

research

∙ 07/05/2022

Rethinking the Importance of Sampling in Physics-informed Neural Networks

Physics-informed neural networks (PINNs) have emerged as a powerful tool...

22 Arka Daw, et al. ∙

research

∙ 06/07/2022

NOMAD: Nonlinear Manifold Decoders for Operator Learning

Supervised learning in function spaces is an emerging area of machine le...

109 Jacob H. Seidman, et al. ∙

research

∙ 03/14/2022

Respecting causality is all you need for training physics-informed neural networks

While the popularity of physics-informed neural networks (PINNs) is stea...

0 Sifan Wang, et al. ∙

research

∙ 03/11/2022

Learning cardiac activation maps from 12-lead ECG with multi-fidelity Bayesian optimization on manifolds

We propose a method for identifying an ectopic activation in the heart n...

0 Simone Pezzuto, et al. ∙

research

∙ 03/06/2022

Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors

We present a simple and effective approach for posterior uncertainty qua...

0 Yibo Yang, et al. ∙

research

∙ 01/28/2022

Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps

We propose FiberNet, a method to estimate in-vivo the cardiac fiber arch...

2 Carlos Ruiz Herrera, et al. ∙

research

∙ 01/04/2022

Learning Operators with Coupled Attention

Supervised operator learning is an emerging machine learning paradigm wi...

13 Georgios Kissas, et al. ∙

research

∙ 10/25/2021

Fast PDE-constrained optimization via self-supervised operator learning

Design and optimal control problems are among the fundamental, ubiquitou...

0 Sifan Wang, et al. ∙

research

∙ 10/04/2021

Improved architectures and training algorithms for deep operator networks

Operator learning techniques have recently emerged as a powerful tool fo...

0 Sifan Wang, et al. ∙

research

∙ 06/09/2021

Long-time integration of parametric evolution equations with physics-informed DeepONets

Ordinary and partial differential equations (ODEs/PDEs) play a paramount...

0 Sifan Wang, et al. ∙

research

∙ 03/19/2021

Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets

Deep operator networks (DeepONets) are receiving increased attention tha...

0 Sifan Wang, et al. ∙

research

∙ 03/04/2021

Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data

This paper presents a machine learning framework (GP-NODE) for Bayesian ...

0 Mohamed Aziz Bhouri, et al. ∙

research

∙ 02/22/2021

Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks

Electroanatomical maps are a key tool in the diagnosis and treatment of ...

0 Thomas Grandits, et al. ∙

research

∙ 02/19/2021

Output-Weighted Sampling for Multi-Armed Bandits with Extreme Payoffs

We present a new type of acquisition functions for online decision makin...

0 Yibo Yang, et al. ∙

research

∙ 12/18/2020

On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks

Physics-informed neural networks (PINNs) are demonstrating remarkable pr...

58 Sifan Wang, et al. ∙

research

∙ 07/28/2020

When and why PINNs fail to train: A neural tangent kernel perspective

Physics-informed neural networks (PINNs) have lately received great atte...

0 Sifan Wang, et al. ∙

research

∙ 06/04/2020

Deep learning of free boundary and Stefan problems

Free boundary problems appear naturally in numerous areas of mathematics...

0 Sifan Wang, et al. ∙

research

∙ 04/15/2020

Bayesian differential programming for robust systems identification under uncertainty

This paper presents a machine learning framework for Bayesian systems id...

0 Yibo Yang, et al. ∙

research

∙ 02/25/2020

Exact artificial boundary conditions of 1D semi-discretized peridynamics

The peridynamic theory reformulates the equations of continuum mechanics...

0 Songsong Ji, et al. ∙

research

∙ 01/13/2020

Understanding and mitigating gradient pathologies in physics-informed neural networks

The widespread use of neural networks across different scientific domain...

18 Sifan Wang, et al. ∙

research

∙ 05/13/2019

Machine learning in cardiovascular flows modeling: Predicting pulse wave propagation from non-invasive clinical measurements using physics-informed deep learning

Advances in computational science offer a principled pipeline for predic...

0 Georgios Kissas, et al. ∙

research

∙ 05/09/2019

Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models

Machine learning techniques typically rely on large datasets to create a...

10 Francisco Sahli Costabal, et al. ∙

research

∙ 04/02/2019

A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations

We investigate the use of discrete and continuous versions of physics-in...

0 Ramakrishna Tipireddy, et al. ∙

research

∙ 01/18/2019

Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data

Surrogate modeling and uncertainty quantification tasks for PDE systems ...

0 Yinhao Zhu, et al. ∙

research

∙ 01/15/2019

Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems

We present a probabilistic deep learning methodology that enables the co...

0 Yibo Yang, et al. ∙

research

∙ 12/09/2018

Physics-informed deep generative models

We consider the application of deep generative models in propagating unc...

0 Yibo Yang, et al. ∙

research

∙ 11/09/2018

Adversarial Uncertainty Quantification in Physics-Informed Neural Networks

We present a deep learning framework for quantifying and propagating unc...

6 Yibo Yang, et al. ∙

research

∙ 08/02/2018

Machine Learning of Space-Fractional Differential Equations

Data-driven discovery of "hidden physics" -- i.e., machine learning of d...

0 Mamikon Gulian, et al. ∙

research

∙ 01/04/2018

Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems

The process of transforming observed data into predictive mathematical m...

0 Maziar Raissi, et al. ∙

research

∙ 11/28/2017

Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations

We introduce physics informed neural networks -- neural networks that ar...

0 Maziar Raissi, et al. ∙

research

∙ 11/28/2017

Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations

We introduce physics informed neural networks -- neural networks that ar...

0 Maziar Raissi, et al. ∙

research

∙ 03/29/2017

Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations

We introduce the concept of numerical Gaussian processes, which we defin...

0 Maziar Raissi, et al. ∙

Paris Perdikaris

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