Pages that link to "Item:Q3130409"
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The following pages link to Numerical Gaussian Processes for Time-Dependent and Nonlinear Partial Differential Equations (Q3130409):
Displaying 50 items.
- Sparse polynomial chaos expansions using variational relevance vector machines (Q781971) (← links)
- A new class of high-order methods for fluid dynamics simulations using Gaussian process modeling: one-dimensional case (Q1668731) (← links)
- Bayesian numerical methods for nonlinear partial differential equations (Q2058795) (← links)
- Optimally weighted loss functions for solving PDEs with neural networks (Q2068635) (← links)
- Interpretable machine learning: fundamental principles and 10 grand challenges (Q2074414) (← links)
- Monte Carlo fPINNs: deep learning method for forward and inverse problems involving high dimensional fractional partial differential equations (Q2083146) (← links)
- Learning ``best'' kernels from data in Gaussian process regression. With application to aerodynamics (Q2083686) (← links)
- The SPDE approach to Matérn fields: graph representations (Q2092895) (← links)
- Bridging the gap: machine learning to resolve improperly modeled dynamics (Q2116291) (← links)
- Active training of physics-informed neural networks to aggregate and interpolate parametric solutions to the Navier-Stokes equations (Q2124408) (← links)
- Randomised one-step time integration methods for deterministic operator differential equations (Q2125034) (← links)
- Augmented Gaussian random field: theory and computation (Q2129158) (← links)
- Using neural networks to accelerate the solution of the Boltzmann equation (Q2132591) (← links)
- System identification through Lipschitz regularized deep neural networks (Q2132640) (← links)
- SPINN: sparse, physics-based, and partially interpretable neural networks for PDEs (Q2133032) (← links)
- Solving and learning nonlinear PDEs with Gaussian processes (Q2133484) (← 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)
- Learning biological dynamics from spatio-temporal data by Gaussian processes (Q2141319) (← links)
- Retracted: Model order reduction method based on machine learning for parameterized time-dependent partial differential equations (Q2161825) (← links)
- Scientific machine learning through physics-informed neural networks: where we are and what's next (Q2162315) (← links)
- Stress-based topology optimization under uncertainty via simulation-based Gaussian process (Q2184305) (← links)
- Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems (Q2184334) (← links)
- Methods to recover unknown processes in partial differential equations using data (Q2210652) (← links)
- Data-driven discovery of PDEs in complex datasets (Q2214651) (← links)
- Adversarial uncertainty quantification in physics-informed neural networks (Q2222278) (← links)
- Approximate Bayesian model inversion for PDEs with heterogeneous and state-dependent coefficients (Q2222341) (← links)
- Physics-informed cokriging: a Gaussian-process-regression-based multifidelity method for data-model convergence (Q2222351) (← links)
- Deep learning of dynamics and signal-noise decomposition with time-stepping constraints (Q2222431) (← links)
- Enforcing constraints for interpolation and extrapolation in generative adversarial networks (Q2222513) (← links)
- Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems (Q2222519) (← links)
- Sparse identification of truncation errors (Q2222522) (← 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)
- Numerical solution and bifurcation analysis of nonlinear partial differential equations with extreme learning machines (Q2236543) (← links)
- Hidden physics model for parameter estimation of elastic wave equations (Q2236961) (← links)
- Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian processes (Q2246340) (← links)
- Bi-directional coupling between a PDE-domain and an adjacent data-domain equipped with multi-fidelity sensors (Q2312107) (← links)
- A hybrid MGA-MSGD ANN training approach for approximate solution of linear elliptic PDEs (Q2666253) (← links)
- Wasserstein generative adversarial uncertainty quantification in physics-informed neural networks (Q2671386) (← links)
- A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems (Q2672767) (← links)
- The deep learning Galerkin method for the general Stokes equations (Q2674271) (← links)
- On the influence of over-parameterization in manifold based surrogates and deep neural operators (Q2687573) (← links)
- An overview on deep learning-based approximation methods for partial differential equations (Q2697278) (← links)
- (Q3791980) (← links)
- Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations (Q4558167) (← links)
- Deep learning of vortex-induced vibrations (Q4647380) (← links)
- Data-Driven Learning of Nonautonomous Systems (Q4997352) (← links)
- (Q4998909) (← links)
- Learning and meta-learning of stochastic advection–diffusion–reaction systems from sparse measurements (Q5014838) (← links)
