The following pages link to PyTorch (Q32752):
Displaying 50 items.
- A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder (Q2134764) (← links)
- An adaptive time-integration scheme for stiff chemistry based on computational singular perturbation and artificial neural networks (Q2134789) (← links)
- On the optimization of approximate control variates with parametrically defined estimators (Q2134796) (← links)
- Learning time-dependent PDEs with a linear and nonlinear separate convolutional neural network (Q2135244) (← links)
- Physics constrained learning for data-driven inverse modeling from sparse observations (Q2135255) (← links)
- Machine learning moment closure models for the radiative transfer equation. I: Directly learning a gradient based closure (Q2135258) (← links)
- Dynamic calibration of differential equations using machine learning, with application to turbulence models (Q2135788) (← links)
- State estimation with limited sensors -- a deep learning based approach (Q2135833) (← links)
- Thermodynamically consistent physics-informed neural networks for hyperbolic systems (Q2136443) (← links)
- Variational inference at glacier scale (Q2137912) (← links)
- A neural network multigrid solver for the Navier-Stokes equations (Q2137963) (← links)
- Normalizing field flows: solving forward and inverse stochastic differential equations using physics-informed flow models (Q2138012) (← links)
- Revealing hidden dynamics from time-series data by ODENet (Q2138013) (← links)
- Feasibility-based fixed point networks (Q2138454) (← links)
- A comparison of neural network architectures for data-driven reduced-order modeling (Q2138791) (← links)
- IGA-reuse-NET: a deep-learning-based isogeometric analysis-reuse approach with topology-consistent parameterization (Q2139715) (← links)
- MuLOT: multi-level optimization of the canonical polyadic tensor decomposition at large-scale (Q2140454) (← links)
- Performance analysis of a dual stage deep rain streak removal convolution neural network module with a modified deep residual dense network (Q2140986) (← links)
- Abstraction of Markov population dynamics via generative adversarial nets (Q2142099) (← links)
- Graph neural networks for simulating crack coalescence and propagation in brittle materials (Q2142205) (← links)
- End-to-end learning for off-road terrain navigation using the chrono open-source simulation platform (Q2142333) (← links)
- Discovery of slow variables in a class of multiscale stochastic systems via neural networks (Q2144224) (← links)
- Exploiting verified neural networks via floating point numerical error (Q2145326) (← links)
- Learning generative neural networks with physics knowledge (Q2146912) (← links)
- Deep learning for the partially linear Cox model (Q2148978) (← links)
- A stochastic extra-step quasi-Newton method for nonsmooth nonconvex optimization (Q2149551) (← links)
- Newton's method, Bellman recursion and differential dynamic programming for unconstrained nonlinear dynamic games (Q2150657) (← links)
- On the landscape of one-hidden-layer sparse networks and beyond (Q2152502) (← links)
- Particle gradient descent model for point process generation (Q2152556) (← links)
- GPU accelerated estimation of a shared random effect joint model for dynamic prediction (Q2157530) (← links)
- An adaptively weighted stochastic gradient MCMC algorithm for Monte Carlo simulation and global optimization (Q2159413) (← links)
- Machine learning for topology optimization: physics-based learning through an independent training strategy (Q2160385) (← links)
- Deep reinforcement learning of viscous incompressible flow (Q2162036) (← links)
- Scientific machine learning through physics-informed neural networks: where we are and what's next (Q2162315) (← links)
- Polynomial-based graph convolutional neural networks for graph classification (Q2163182) (← links)
- Few-shot learning for spatial regression via neural embedding-based Gaussian processes (Q2163183) (← links)
- Lipschitzness is all you need to tame off-policy generative adversarial imitation learning (Q2163202) (← links)
- Modelling spatiotemporal dynamics from Earth observation data with neural differential equations (Q2163266) (← links)
- Metagenomics binning of long reads using read-overlap graphs (Q2163969) (← links)
- Data-driven rogue waves and parameters discovery in nearly integrable \(\mathcal{PT}\)-symmetric Gross-Pitaevskii equations via PINNs deep learning (Q2167994) (← links)
- Stochastic physics-informed neural ordinary differential equations (Q2168292) (← links)
- Physics-informed distribution transformers via molecular dynamics and deep neural networks (Q2168329) (← links)
- Probabilistic programming with stochastic variational message passing (Q2169202) (← links)
- Learning pseudo-backdoors for mixed integer programs (Q2170189) (← links)
- Deep policy dynamic programming for vehicle routing problems (Q2170197) (← links)
- Training thinner and deeper neural networks: jumpstart regularization (Q2170213) (← links)
- Numerical approaches for investigating quasiconvexity in the context of Morrey's conjecture (Q2171039) (← links)
- HomPINNs: Homotopy physics-informed neural networks for learning multiple solutions of nonlinear elliptic differential equations (Q2172562) (← links)
- Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (Q2176917) (← links)
- A neurally-guided, parallel theorem prover (Q2180215) (← links)
