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Found 25 papers, 1 papers with code
Multifidelity domain decomposition-based physics-informed neural networks for time-dependent problems
Multiscale problems are challenging for neural network-based discretizations of differential equations, such as physics-informed neural networks (PINNs).
Rethinking Skip Connections in Spiking Neural Networks with Time-To-First-Spike Coding
In this work, we delve into the role of skip connections, a widely used concept in Artificial Neural Networks (ANNs), within the domain of SNNs with TTFS coding.
Stacked networks improve physics-informed training: applications to neural networks and deep operator networks
The equations imposed at each step of the iterative process can be the same or different (akin to simulated annealing).
Efficient kernel surrogates for neural network-based regression
For the regression problem of smooth functions and logistic regression classification, we show that the CK performance is only marginally worse than that of the NTK and, in certain cases, is shown to be superior.
Exploring Learned Representations of Neural Networks with Principal Component Analysis
Understanding feature representation for deep neural networks (DNNs) remains an open question within the general field of explainable AI.
Physics-informed machine learning of the correlation functions in bulk fluids
The Ornstein-Zernike (OZ) equation is the fundamental equation for pair correlation function computations in the modern integral equation theory for liquids.
Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model
To solve the 2D model with the PINN approach, a composite neural network is employed to approximate species concentration and potentials; the input and output are normalized according to prior knowledge of the battery system; the governing equations and boundary conditions are first scaled to an order of magnitude around 1, and then further balanced with a self-weighting method.
A multifidelity approach to continual learning for physical systems
We introduce a novel continual learning method based on multifidelity deep neural networks.
Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations
Physics-informed neural networks have emerged as an alternative method for solving partial differential equations.
A Multifidelity deep operator network approach to closure for multiscale systems
Projection-based reduced order models (PROMs) have shown promise in representing the behavior of multiscale systems using a small set of generalized (or latent) variables.
ViTO: Vision Transformer-Operator
We combine vision transformers with operator learning to solve diverse inverse problems described by partial differential equations (PDEs).
SDYN-GANs: Adversarial Learning Methods for Multistep Generative Models for General Order Stochastic Dynamics
We introduce adversarial learning methods for data-driven generative modeling of the dynamics of $n^{th}$-order stochastic systems.
A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling
One of the most challenging and consequential problems in climate modeling is to provide probabilistic projections of sea level rise.
SMS: Spiking Marching Scheme for Efficient Long Time Integration of Differential Equations
We propose a Spiking Neural Network (SNN)-based explicit numerical scheme for long time integration of time-dependent Ordinary and Partial Differential Equations (ODEs, PDEs).
Multifidelity Deep Operator Networks For Data-Driven and Physics-Informed Problems
We demonstrate the new multi-fidelity training in diverse examples, including modeling of the ice-sheet dynamics of the Humboldt glacier, Greenland, using two different fidelity models and also using the same physical model at two different resolutions.
Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery
To improve the accuracy of voltage prediction at extreme ranges, we introduce a second (enhanced) DNN to mitigate the prediction errors carried from the 0D model itself and call the resulting approach enhanced PCDNN (ePCDNN).
Machine-learning custom-made basis functions for partial differential equations
In particular, we use a deep learning technique known as the Deep Operator Network (DeepONet), to identify candidate functions on which to expand the solution of PDEs.
Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling
Discovery of dynamical systems from data forms the foundation for data-driven modeling and recently, structure-preserving geometric perspectives have been shown to provide improved forecasting, stability, and physical realizability guarantees.
Machine learning structure preserving brackets for forecasting irreversible processes
Forecasting of time-series data requires imposition of inductive biases to obtain predictive extrapolation, and recent works have imposed Hamiltonian/Lagrangian form to preserve structure for systems with reversible dynamics.
Physics-constrained deep neural network method for estimating parameters in a redox flow battery
In this paper, we present a physics-constrained deep neural network (PCDNN) method for parameter estimation in the zero-dimensional (0D) model of the vanadium redox flow battery (VRFB).
Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning
We present a collection of results on how to enforce constraints coming from the dynamical system in order to accelerate the training of deep neural networks to represent the flow map of the system as well as increase their predictive ability.
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-informed neural network methods for learning unknown dynamics or constitutive relations of a dynamical system.
Doing the impossible: Why neural networks can be trained at all
We show that adding structure to the neural network that enforces higher mutual information between layers speeds training and leads to more accurate results.
Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks
We suggest ways to enforce given constraints in the output of a Generative Adversarial Network (GAN) generator both for interpolation and extrapolation (prediction).
Solving differential equations with unknown constitutive relations as recurrent neural networks
We solve a system of ordinary differential equations with an unknown functional form of a sink (reaction rate) term.
