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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...
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08/16/2023
An Expert's Guide to Training Physics-informed Neural Networks
Physics-informed neural networks (PINNs) have been popularized as a deep...
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08/10/2023
PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers
Time-dependent partial differential equations (PDEs) are ubiquitous in s...
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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...
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05/15/2023
Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior
Data-driven approaches achieve remarkable results for the modeling of co...
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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...
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02/20/2023
Variational Autoencoding Neural Operators
Unsupervised learning with functional data is an emerging paradigm of ma...
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02/14/2023
Scalable Bayesian optimization with high-dimensional outputs using randomized prior networks
Several fundamental problems in science and engineering consist of globa...
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10/03/2022
Random Weight Factorization Improves the Training of Continuous Neural Representations
Continuous neural representations have recently emerged as a powerful an...
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09/08/2022
Δ-PINNs: physics-informed neural networks on complex geometries
Physics-informed neural networks (PINNs) have demonstrated promise in so...
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09/06/2022
Semi-supervised Invertible DeepONets for Bayesian Inverse Problems
Deep Operator Networks (DeepONets) offer a powerful, data-driven tool fo...
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07/05/2022
Rethinking the Importance of Sampling in Physics-informed Neural Networks
Physics-informed neural networks (PINNs) have emerged as a powerful tool...
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06/07/2022
NOMAD: Nonlinear Manifold Decoders for Operator Learning
Supervised learning in function spaces is an emerging area of machine le...
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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...
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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...
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03/06/2022
Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors
We present a simple and effective approach for posterior uncertainty qua...
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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...
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01/04/2022
Learning Operators with Coupled Attention
Supervised operator learning is an emerging machine learning paradigm wi...
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10/25/2021
Fast PDE-constrained optimization via self-supervised operator learning
Design and optimal control problems are among the fundamental, ubiquitou...
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10/04/2021
Improved architectures and training algorithms for deep operator networks
Operator learning techniques have recently emerged as a powerful tool fo...
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06/09/2021
Long-time integration of parametric evolution equations with physics-informed DeepONets
Ordinary and partial differential equations (ODEs/PDEs) play a paramount...
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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...
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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 ...
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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 ...
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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...
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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...
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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...
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06/04/2020
Deep learning of free boundary and Stefan problems
Free boundary problems appear naturally in numerous areas of mathematics...
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04/15/2020
Bayesian differential programming for robust systems identification under uncertainty
This paper presents a machine learning framework for Bayesian systems id...
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02/25/2020
Exact artificial boundary conditions of 1D semi-discretized peridynamics
The peridynamic theory reformulates the equations of continuum mechanics...
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01/13/2020
Understanding and mitigating gradient pathologies in physics-informed neural networks
The widespread use of neural networks across different scientific domain...
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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...
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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...
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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...
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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 ...
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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...
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12/09/2018
Physics-informed deep generative models
We consider the application of deep generative models in propagating unc...
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11/09/2018
Adversarial Uncertainty Quantification in Physics-Informed Neural Networks
We present a deep learning framework for quantifying and propagating unc...
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08/02/2018
Machine Learning of Space-Fractional Differential Equations
Data-driven discovery of "hidden physics" -- i.e., machine learning of d...
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01/04/2018
Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems
The process of transforming observed data into predictive mathematical m...
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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...
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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...
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03/29/2017