The following pages link to (Q4558473):
Displaying 50 items.
- CAN-PINN: a fast physics-informed neural network based on coupled-automatic-numerical differentiation method (Q2142144) (← links)
- Learning generative neural networks with physics knowledge (Q2146912) (← links)
- Improved architectures and training algorithms for deep operator networks (Q2149522) (← links)
- Novel first and second order numerical differentiation techniques and their application to nonlinear analysis of Kirchhoff-Love shells (Q2150252) (← links)
- Physics-informed neural networks for inverse problems in supersonic flows (Q2157127) (← links)
- Bayesian-EUCLID: discovering hyperelastic material laws with uncertainties (Q2160432) (← links)
- Variational physics informed neural networks: the role of quadratures and test functions (Q2162334) (← links)
- Zeroth-order methods for noisy Hölder-gradient functions (Q2162695) (← links)
- Deep neural networks based temporal-difference methods for high-dimensional parabolic partial differential equations (Q2168314) (← links)
- Deep learning solver for solving advection-diffusion equation in comparison to finite difference methods (Q2170855) (← 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)
- Physics-informed neural networks for high-speed flows (Q2175317) (← links)
- Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (Q2176917) (← links)
- Accelerating multiscale finite element simulations of history-dependent materials using a recurrent neural network (Q2179209) (← links)
- Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems (Q2184334) (← links)
- Deep neural network approach to forward-inverse problems (Q2197226) (← links)
- Computation of higher order Lie derivatives on the infinity computer (Q2199789) (← links)
- Topology optimization based on deep representation learning (DRL) for compliance and stress-constrained design (Q2205158) (← links)
- An optimized cognitive-assisted machine translation approach for natural language processing (Q2218516) (← links)
- Adaptive activation functions accelerate convergence in deep and physics-informed neural networks (Q2223034) (← links)
- Data-driven rogue waves and parameter discovery in the defocusing nonlinear Schrödinger equation with a potential using the PINN deep learning (Q2233120) (← links)
- Unsupervised discovery of interpretable hyperelastic constitutive laws (Q2237006) (← links)
- On the eigenvector bias of Fourier feature networks: from regression to solving multi-scale PDEs with physics-informed neural networks (Q2237440) (← links)
- Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture (Q2237458) (← links)
- Solving inverse problems in stochastic models using deep neural networks and adversarial training (Q2237477) (← links)
- A Simulink-based software solution using the infinity computer methodology for higher order differentiation (Q2244122) (← links)
- Learning viscoelasticity models from indirect data using deep neural networks (Q2246355) (← links)
- Local extreme learning machines and domain decomposition for solving linear and nonlinear partial differential equations (Q2246361) (← links)
- Data-driven vector soliton solutions of coupled nonlinear Schrödinger equation using a deep learning algorithm (Q2246919) (← links)
- Accelerated directional search with non-Euclidean prox-structure (Q2290400) (← links)
- Comparing gradient descent with automatic differentiation and particle swarm optimization techniques for estimating tumor blood flow parameters in contrast-enhanced imaging (Q2291881) (← links)
- Black-box learning of multigrid parameters (Q2291999) (← links)
- Neural network as a function approximator and its application in solving differential equations (Q2313205) (← links)
- Automatic multivector differentiation and optimization (Q2361067) (← links)
- A Hitchhiker's guide to automatic differentiation (Q2630759) (← links)
- Poisoning finite-horizon Markov decision processes at design time (Q2668608) (← links)
- Physics-informed Karhunen-Loéve and neural network approximations for solving inverse differential equation problems (Q2671323) (← links)
- A-PINN: auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations (Q2671335) (← links)
- Wasserstein generative adversarial uncertainty quantification in physics-informed neural networks (Q2671386) (← links)
- On computing the hyperparameter of extreme learning machines: algorithm and application to computational PDEs, and comparison with classical and high-order finite elements (Q2671403) (← links)
- Mixed Deep Gaussian Mixture Model: a clustering model for mixed datasets (Q2673358) (← links)
- Improved deep neural networks with domain decomposition in solving partial differential equations (Q2674166) (← links)
- A discontinuity capturing shallow neural network for elliptic interface problems (Q2675625) (← links)
- A-WPINN algorithm for the data-driven vector-soliton solutions and parameter discovery of general coupled nonlinear equations (Q2677793) (← links)
- Parallelized integrated nested Laplace approximations for fast Bayesian inference (Q2677902) (← links)
- Integrated finite element neural network (I-FENN) for non-local continuum damage mechanics (Q2678488) (← links)
- Thermodynamically consistent machine-learned internal state variable approach for data-driven modeling of path-dependent materials (Q2679297) (← links)
- Extension of dynamic mode decomposition for dynamic systems with incomplete information based on t-model of optimal prediction (Q2681120) (← links)
- Physics-informed neural networks combined with polynomial interpolation to solve nonlinear partial differential equations (Q2682670) (← links)
