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08/31/2023
Artificial to Spiking Neural Networks Conversion for Scientific Machine Learning
We introduce a method to convert Physics-Informed Neural Networks (PINNs...
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08/09/2023
Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operators
We address the challenge of sound propagation simulations in 3D virtual ...
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07/23/2023
Tackling the Curse of Dimensionality with Physics-Informed Neural Networks
The curse-of-dimensionality (CoD) taxes computational resources heavily ...
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07/18/2023
Characterization of partial wetting by CMAS droplets using multiphase many-body dissipative particle dynamics and data-driven discovery based on PINNs
The molten sand, a mixture of calcia, magnesia, alumina, and silicate, k...
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07/18/2023
DiTTO: Diffusion-inspired Temporal Transformer Operator
Solving partial differential equations (PDEs) using a data-driven approa...
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07/16/2023
Discovering a reaction-diffusion model for Alzheimer's disease by combining PINNs with symbolic regression
Misfolded tau proteins play a critical role in the progression and patho...
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07/05/2023
TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers
Dynamic graph embedding has emerged as a very effective technique for ad...
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07/01/2023
Residual-based attention and connection to information bottleneck theory in PINNs
Driven by the need for more efficient and seamless integration of physic...
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06/27/2023
CrunchGPT: A chatGPT assisted framework for scientific machine learning
Scientific Machine Learning (SciML) has advanced recently across many di...
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05/18/2023
A Framework Based on Symbolic Regression Coupled with eXtended Physics-Informed Neural Networks for Gray-Box Learning of Equations of Motion from Data
We propose a framework and an algorithm to uncover the unknown parts of ...
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05/04/2023
A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimes
Quantifying biomechanical properties of the human vasculature could deep...
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04/15/2023
Learning in latent spaces improves the predictive accuracy of deep neural operators
Operator regression provides a powerful means of constructing discretiza...
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03/22/2023
Leveraging Multi-time Hamilton-Jacobi PDEs for Certain Scientific Machine Learning Problems
Hamilton-Jacobi partial differential equations (HJ PDEs) have deep conne...
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03/20/2023
Bi-orthogonal fPINN: A physics-informed neural network method for solving time-dependent stochastic fractional PDEs
Fractional partial differential equations (FPDEs) can effectively repres...
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03/19/2023
LNO: Laplace Neural Operator for Solving Differential Equations
We introduce the Laplace neural operator (LNO), which leverages the Lapl...
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03/15/2023
ViTO: Vision Transformer-Operator
We combine vision transformers with operator learning to solve diverse i...
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03/03/2023
Neural Operator Learning for Long-Time Integration in Dynamical Systems with Recurrent Neural Networks
Deep neural networks are an attractive alternative for simulating comple...
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02/28/2023
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions
Physics-informed neural networks (PINNs) as a means of solving partial d...
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02/23/2023
Learning stiff chemical kinetics using extended deep neural operators
We utilize neural operators to learn the solution propagator for the cha...
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02/07/2023
Learning bias corrections for climate models using deep neural operators
Numerical simulation for climate modeling resolving all important scales...
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02/02/2023
Deep neural operators can serve as accurate surrogates for shape optimization: A case study for airfoils
Deep neural operators, such as DeepONets, have changed the paradigm in h...
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01/26/2023
A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling
One of the most challenging and consequential problems in climate modeli...
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01/05/2023
L-HYDRA: Multi-Head Physics-Informed Neural Networks
We introduce multi-head neural networks (MH-NNs) to physics-informed mac...
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12/13/2022
Reliable extrapolation of deep neural operators informed by physics or sparse observations
Deep neural operators can learn nonlinear mappings between infinite-dime...
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11/17/2022
SMS: Spiking Marching Scheme for Efficient Long Time Integration of Differential Equations
We propose a Spiking Neural Network (SNN)-based explicit numerical schem...
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11/16/2022
Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology
In this paper, we propose the augmented physics-informed neural network ...
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10/31/2022
On the Geometry Transferability of the Hybrid Iterative Numerical Solver for Differential Equations
The discovery of fast numerical solvers prompted a clear and rapid shift...
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09/06/2022
How important are activation functions in regression and classification? A survey, performance comparison, and future directions
Inspired by biological neurons, the activation functions play an essenti...
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08/28/2022
A Hybrid Iterative Numerical Transferable Solver (HINTS) for PDEs Based on Deep Operator Network and Relaxation Methods
Iterative solvers of linear systems are a key component for the numerica...
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08/21/2022
G2Φnet: Relating Genotype and Biomechanical Phenotype of Tissues with Deep Learning
Many genetic mutations adversely affect the structure and function of lo...
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07/08/2022
Physics-Informed Deep Neural Operator Networks
Standard neural networks can approximate general nonlinear operators, re...
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05/23/2022
Fractional SEIR Model and Data-Driven Predictions of COVID-19 Dynamics of Omicron Variant
We study the dynamic evolution of COVID-19 cased by the Omicron variant ...
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05/17/2022
Function Regression using Spiking DeepONet
One of the main broad applications of deep learning is function regressi...
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05/16/2022
Scalable algorithms for physics-informed neural and graph networks
Physics-informed machine learning (PIML) has emerged as a promising new ...
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05/08/2022
Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms
Thoracic aortic aneurysm (TAA) is a localized dilatation of the aorta re...
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04/20/2022
Deep transfer learning for partial differential equations under conditional shift with DeepONet
Traditional machine learning algorithms are designed to learn in isolati...
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04/11/2022
Learning two-phase microstructure evolution using neural operators and autoencoder architectures
Phase-field modeling is an effective mesoscale method for capturing the ...
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04/05/2022
Discovering and forecasting extreme events via active learning in neural operators
Extreme events in society and nature, such as pandemic spikes or rogue w...
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03/09/2022
On the influence of over-parameterization in manifold based surrogates and deep neural operators
Constructing accurate and generalizable approximators for complex physic...
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02/25/2022
Interfacing Finite Elements with Deep Neural Operators for Fast Multiscale Modeling of Mechanics Problems
Multiscale modeling is an effective approach for investigating multiphys...
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02/23/2022
Physics-informed neural networks for inverse problems in supersonic flows
Accurate solutions to inverse supersonic compressible flow problems are ...
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02/07/2022
Deep learning of inverse water waves problems using multi-fidelity data: Application to Serre-Green-Naghdi equations
We consider strongly-nonlinear and weakly-dispersive surface water waves...
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02/03/2022
Systems Biology: Identifiability analysis and parameter identification via systems-biology informed neural networks
The dynamics of systems biological processes are usually modeled by a sy...
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01/14/2022
SympOCnet: Solving optimal control problems with applications to high-dimensional multi-agent path planning problems
Solving high-dimensional optimal control problems in real-time is an imp...
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09/28/2021
DynG2G: An Efficient Stochastic Graph Embedding Method for Temporal Graphs
Dynamic graph embedding has gained great attention recently due to its c...
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09/20/2021
When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization?
Physics-informed neural networks (PINNs) have become a popular choice fo...
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08/31/2021
GFINNs: GENERIC Formalism Informed Neural Networks for Deterministic and Stochastic Dynamical Systems
We propose the GENERIC formalism informed neural networks (GFINNs) that ...
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08/25/2021
Simulating progressive intramural damage leading to aortic dissection using an operator-regression neural network
Aortic dissection progresses via delamination of the medial layer of the...
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07/12/2021
Meta-learning PINN loss functions
We propose a meta-learning technique for offline discovery of physics-in...
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06/05/2021