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Found 15 papers, 7 papers with code
DON-LSTM: Multi-Resolution Learning with DeepONets and Long Short-Term Memory Neural Networks
Deep operator networks (DeepONets, DONs) offer a distinct advantage over traditional neural networks in their ability to be trained on multi-resolution data.
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 rooms with moving sources, which have applications in virtual/augmented reality, game audio, and spatial computing.
Learning in latent spaces improves the predictive accuracy of deep neural operators
Operator regression provides a powerful means of constructing discretization-invariant emulators for partial-differential equations (PDEs) describing physical systems.
Real-Time Prediction of Gas Flow Dynamics in Diesel Engines using a Deep Neural Operator Framework
As an alternative to physics based models, we develop an operator-based regression model (DeepONet) to learn the relevant output states for a mean-value gas flow engine model using the engine operating conditions as input variables.
LNO: Laplace Neural Operator for Solving Differential Equations
Herein, we demonstrate the superior approximation accuracy of a single Laplace layer in LNO over four Fourier modules in FNO in approximating the solutions of three ODEs (Duffing oscillator, driven gravity pendulum, and Lorenz system) and three PDEs (Euler-Bernoulli beam, diffusion equation, and reaction-diffusion system).
Neural Operator Learning for Long-Time Integration in Dynamical Systems with Recurrent Neural Networks
Deep neural networks are an attractive alternative for simulating complex dynamical systems, as in comparison to traditional scientific computing methods, they offer reduced computational costs during inference and can be trained directly from observational data.
Learning stiff chemical kinetics using extended deep neural operators
Specifically, to train the DeepONet for the syngas model, we solve the skeletal kinetic model for different initial conditions.
Physics-Informed Deep Neural Operator Networks
Standard neural networks can approximate general nonlinear operators, represented either explicitly by a combination of mathematical operators, e. g., in an advection-diffusion-reaction partial differential equation, or simply as a black box, e. g., a system-of-systems.
Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms
Thoracic aortic aneurysm (TAA) is a localized dilatation of the aorta resulting from compromised wall composition, structure, and function, which can lead to life-threatening dissection or rupture.
Deep transfer operator learning for partial differential equations under conditional shift
Transfer learning (TL) enables the transfer of knowledge gained in learning to perform one task (source) to a related but different task (target), hence addressing the expense of data acquisition and labeling, potential computational power limitations, and dataset distribution mismatches.
Learning two-phase microstructure evolution using neural operators and autoencoder architectures
We utilize the convolutional autoencoder to provide a compact representation of the microstructure data in a low-dimensional latent space.
On the influence of over-parameterization in manifold based surrogates and deep neural operators
In contrast, an even highly over-parameterized DeepONet leads to better generalization for both smooth and non-smooth dynamics.
A physics-informed variational DeepONet for predicting the crack path in brittle materials
We propose a physics-informed variational formulation of DeepONet (V-DeepONet) for brittle fracture analysis.
An Energy Approach to the Solution of Partial Differential Equations in Computational Mechanics via Machine Learning: Concepts, Implementation and Applications
In this contribution, we explore Deep Neural Networks (DNNs) as an option for approximation.
Transfer learning enhanced physics informed neural network for phase-field modeling of fracture
While most of the PINN algorithms available in the literature minimize the residual of the governing partial differential equation, the proposed approach takes a different path by minimizing the variational energy of the system.
