The following pages link to DeepONet (Q1352890):
Displaying 50 items.
- A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures (Q2237801) (← links)
- Conditional physics informed neural networks (Q2247060) (← links)
- A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials (Q2670380) (← links)
- Adaptive deep neural networks methods for high-dimensional partial differential equations (Q2671349) (← links)
- Wasserstein generative adversarial uncertainty quantification in physics-informed neural networks (Q2671386) (← links)
- Structure preservation for the deep neural network multigrid solver (Q2672194) (← links)
- DeepParticle: learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method (Q2672762) (← links)
- A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems (Q2672767) (← links)
- Scalable uncertainty quantification for deep operator networks using randomized priors (Q2674111) (← links)
- Improved deep neural networks with domain decomposition in solving partial differential equations (Q2674166) (← links)
- Nonlocal kernel network (NKN): a stable and resolution-independent deep neural network (Q2675608) (← links)
- Output-weighted and relative entropy loss functions for deep learning precursors of extreme events (Q2677801) (← links)
- Wavelet neural operator for solving parametric partial differential equations in computational mechanics problems (Q2678512) (← links)
- Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems (Q2679283) (← links)
- Learning high-dimensional parametric maps via reduced basis adaptive residual networks (Q2679335) (← links)
- Neural networks in Fréchet spaces (Q2679424) (← links)
- SVD perspectives for augmenting DeepONet flexibility and interpretability (Q2679470) (← links)
- Data-driven soliton mappings for integrable fractional nonlinear wave equations via deep learning with Fourier neural operator (Q2679950) (← links)
- Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons (Q2681129) (← links)
- Long-time integration of parametric evolution equations with physics-informed DeepONets (Q2683074) (← links)
- A deep Fourier residual method for solving PDEs using neural networks (Q2683430) (← links)
- Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios (Q2683433) (← links)
- Neural network architectures using min-plus algebra for solving certain high-dimensional optimal control problems and Hamilton-Jacobi PDEs (Q2683498) (← links)
- Physics-informed neural network methods based on Miura transformations and discovery of new localized wave solutions (Q2683577) (← links)
- Optimal control by deep learning techniques and its applications on epidemic models (Q2684035) (← links)
- Stochastic projection based approach for gradient free physics informed learning (Q2686876) (← links)
- Data-driven control of agent-based models: an equation/variable-free machine learning approach (Q2687520) (← links)
- On the influence of over-parameterization in manifold based surrogates and deep neural operators (Q2687573) (← links)
- Data-driven forward and inverse problems for chaotic and hyperchaotic dynamic systems based on two machine learning architectures (Q2688074) (← links)
- Local approximation of operators (Q2689140) (← links)
- opPINN: physics-informed neural network with operator learning to approximate solutions to the Fokker-Planck-Landau equation (Q2689626) (← links)
- An unsupervised latent/output physics-informed convolutional-LSTM network for solving partial differential equations using peridynamic differential operator (Q2693426) (← links)
- A general deep transfer learning framework for predicting the flow field of airfoils with small data (Q2698703) (← links)
- Physics informed neural networks: a case study for gas transport problems (Q2699348) (← links)
- Greedy training algorithms for neural networks and applications to PDEs (Q2699382) (← links)
- BI-GreenNet: learning Green's functions by boundary integral network (Q2699491) (← links)
- The Random Feature Model for Input-Output Maps between Banach Spaces (Q3382802) (← links)
- A multi-level procedure for enhancing accuracy of machine learning algorithms (Q5014840) (← links)
- Concurrent MultiParameter Learning Demonstrated on the Kuramoto--Sivashinsky Equation (Q5038404) (← links)
- PFNN-2: A Domain Decomposed Penalty-Free Neural Network Method for Solving Partial Differential Equations (Q5045670) (← links)
- MIONet: Learning Multiple-Input Operators via Tensor Product (Q5048574) (← links)
- A deep neural network-based numerical method for solving contact problems (Q5052594) (← links)
- (Q5053337) (← links)
- (Q5054645) (← links)
- Two-Layer Neural Networks with Values in a Banach Space (Q5055293) (← links)
- SympOCnet: Solving Optimal Control Problems with Applications to High-Dimensional Multiagent Path Planning Problems (Q5058288) (← links)
- A deep learning approach to Reduced Order Modelling of parameter dependent partial differential equations (Q5058646) (← links)
- Modern Koopman Theory for Dynamical Systems (Q5075835) (← links)
- Physics Informed Neural Networks (PINNs) For Approximating Nonlinear Dispersive PDEs (Q5079535) (← links)
- Generative Adversarial Network for Probabilistic Forecast of Random Dynamical Systems (Q5095487) (← links)
