The following pages link to PyTorch (Q32752):
Displaying 50 items.
- Constraint learning for control tasks with limited duration barrier functions (Q2664234) (← links)
- Kernel-based methods for Volterra series identification (Q2665176) (← links)
- Imperfect imaGANation: implications of GANs exacerbating biases on facial data augmentation and snapchat face lenses (Q2667840) (← links)
- High Reynolds number airfoil turbulence modeling method based on machine learning technique (Q2670056) (← links)
- A deep learning approach for the transonic flow field predictions around airfoils (Q2670066) (← links)
- A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials (Q2670380) (← links)
- A-PINN: auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations (Q2671335) (← links)
- A gradient-based deep neural network model for simulating multiphase flow in porous media (Q2671397) (← links)
- Physics and equality constrained artificial neural networks: application to forward and inverse problems with multi-fidelity data fusion (Q2671417) (← links)
- A deep learning energy method for hyperelasticity and viscoelasticity (Q2671703) (← links)
- An algorithm selection approach for the flexible job shop scheduling problem: choosing constraint programming solvers through machine learning (Q2672113) (← links)
- Structure preservation for the deep neural network multigrid solver (Q2672194) (← links)
- A machine learning framework for LES closure terms (Q2672196) (← links)
- Determining kernels in linear viscoelasticity (Q2672776) (← links)
- Machine learning the real discriminant locus (Q2674017) (← links)
- A molecular-continuum multiscale model for inviscid liquid-vapor flow with sharp interfaces (Q2675620) (← links)
- Derivatives of feed-forward neural networks and their application in real-time market risk management (Q2676274) (← links)
- A-WPINN algorithm for the data-driven vector-soliton solutions and parameter discovery of general coupled nonlinear equations (Q2677793) (← links)
- Quarks and gluons in the Lund plane (Q2678114) (← links)
- Geometric learning for computational mechanics. II: Graph embedding for interpretable multiscale plasticity (Q2678490) (← links)
- Statistically equivalent surrogate material models: impact of random imperfections on the elasto-plastic response (Q2679290) (← links)
- Thermodynamically consistent machine-learned internal state variable approach for data-driven modeling of path-dependent materials (Q2679297) (← links)
- PI-VAE: physics-informed variational auto-encoder for stochastic differential equations (Q2679439) (← links)
- Isogeometric analysis-based physics-informed graph neural network for studying traffic jam in neurons (Q2679502) (← links)
- Data-driven soliton mappings for integrable fractional nonlinear wave equations via deep learning with Fourier neural operator (Q2679950) (← links)
- Design of low-artifact interpolation kernels by means of computer algebra (Q2680119) (← links)
- Machine learning moment closure models for the radiative transfer equation. III: enforcing hyperbolicity and physical characteristic speeds (Q2680325) (← links)
- Logic explained networks (Q2680793) (← links)
- CPINNs: a coupled physics-informed neural networks for the closed-loop geothermal system (Q2682678) (← links)
- Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios (Q2683433) (← links)
- Distance-preserving manifold denoising for data-driven mechanics (Q2683440) (← links)
- Quartic gauge-Higgs couplings: constraints and future directions (Q2683986) (← links)
- Physics-integrated neural differentiable (PiNDiff) model for composites manufacturing (Q2686904) (← links)
- Side effects of learning from low-dimensional data embedded in a Euclidean space (Q2687305) (← links)
- Model reduction for the material point method via an implicit neural representation of the deformation map (Q2687512) (← links)
- A physics-informed diffusion model for high-fidelity flow field reconstruction (Q2687536) (← links)
- Evolution strategies: application in hybrid quantum-classical neural networks (Q2687671) (← links)
- Few-shot learning based blind parameter estimation for multiple frequency-hopping signals (Q2688033) (← links)
- DNA-binding protein prediction based on deep transfer learning (Q2688384) (← links)
- Convex and concave envelopes of artificial neural network activation functions for deterministic global optimization (Q2689856) (← links)
- Enhancing phenomenological yield functions with data: challenges and opportunities (Q2692822) (← links)
- Numerical upscaling of parametric microstructures in a possibilistic uncertainty framework with tensor trains (Q2692897) (← links)
- Finite strain poro-hyperelasticity: an asymptotic multi-scale ALE-FSI approach supported by ANNs (Q2692904) (← links)
- Thermodynamics-informed neural networks for physically realistic mixed reality (Q2693393) (← links)
- Modular machine learning-based elastoplasticity: generalization in the context of limited data (Q2693407) (← links)
- Consistency of randomized integration methods (Q2693691) (← links)
- Deep energy method in topology optimization applications (Q2694685) (← links)
- Automated feature selection procedure for particle jet classification (Q2698965) (← links)
- PDE-constrained models with neural network terms: optimization and global convergence (Q2699336) (← links)
- Learning invariance preserving moment closure model for Boltzmann-BGK equation (Q2699490) (← links)
