289 results on '"Karniadakis, George"'
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2. Two-stage initial-value iterative physics-informed neural networks for simulating solitary waves of nonlinear wave equations
3. Correcting model misspecification in physics-informed neural networks (PINNs)
4. Multifidelity deep operator networks for data-driven and physics-informed problems
5. A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions
6. A hybrid deep neural operator/finite element method for ice-sheet modeling
7. High-order methods for hypersonic flows with strong shocks and real chemistry
8. On the influence of over-parameterization in manifold based surrogates and deep neural operators
9. Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons
10. Neural operator prediction of linear instability waves in high-speed boundary layers
11. Physics-informed neural networks for inverse problems in supersonic flows
12. A spectral method for stochastic fractional PDEs using dynamically-orthogonal/bi-orthogonal decomposition
13. Meta-learning PINN loss functions
14. Learning functional priors and posteriors from data and physics
15. DeepM&Mnet for hypersonics: Predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators
16. Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation
17. Parallel physics-informed neural networks via domain decomposition
18. Multi-fidelity Bayesian neural networks: Algorithms and applications
19. DeepM&Mnet: Inferring the electroconvection multiphysics fields based on operator approximation by neural networks
20. A phase-field method for boiling heat transfer
21. Active- and transfer-learning applied to microscale-macroscale coupling to simulate viscoelastic flows
22. NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations
23. B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data
24. Physics-informed semantic inpainting: Application to geostatistical modeling
25. A stabilized semi-implicit Fourier spectral method for nonlinear space-fractional reaction-diffusion equations
26. Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
27. What is the fractional Laplacian? A comparative review with new results
28. A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems
29. Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems
30. A stabilized phase-field method for two-phase flow at high Reynolds number and large density/viscosity ratio
31. Supervised parallel-in-time algorithm for long-time Lagrangian simulations of stochastic dynamics: Application to hydrodynamics
32. Neural-net-induced Gaussian process regression for function approximation and PDE solution
33. Fractional magneto-hydrodynamics: Algorithms and applications
34. Bi-directional coupling between a PDE-domain and an adjacent Data-domain equipped with multi-fidelity sensors
35. Active learning of constitutive relation from mesoscopic dynamics for macroscopic modeling of non-Newtonian flows
36. Hidden physics models: Machine learning of nonlinear partial differential equations
37. A dissipative particle dynamics method for arbitrarily complex geometries
38. Discovering variable fractional orders of advection–dispersion equations from field data using multi-fidelity Bayesian optimization
39. Machine learning of linear differential equations using Gaussian processes
40. A general CFD framework for fault-resilient simulations based on multi-resolution information fusion
41. A resilient and efficient CFD framework: Statistical learning tools for multi-fidelity and heterogeneous information fusion
42. A robust bi-orthogonal/dynamically-orthogonal method using the covariance pseudo-inverse with application to stochastic flow problems
43. Fractional Burgers equation with nonlinear non-locality: Spectral vanishing viscosity and local discontinuous Galerkin methods
44. Anisotropic single-particle dissipative particle dynamics model
45. Inferring solutions of differential equations using noisy multi-fidelity data
46. Systematic parameter inference in stochastic mesoscopic modeling
47. Fractional modeling of viscoelasticity in 3D cerebral arteries and aneurysms
48. Fast difference schemes for solving high-dimensional time-fractional subdiffusion equations
49. Flow in complex domains simulated by Dissipative Particle Dynamics driven by geometry-specific body-forces
50. Quantification of sampling uncertainty for molecular dynamics simulation: Time-dependent diffusion coefficient in simple fluids
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