1,083 results on '"Karniadakis, George"'
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2. Enhancing severe hypoglycemia prediction in type 2 diabetes mellitus through multi-view co-training machine learning model for imbalanced dataset
3. Rethinking materials simulations: Blending direct numerical simulations with neural operators
4. Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems
5. Real-time prediction of gas flow dynamics in diesel engines using a deep neural operator framework
6. AI-Lorenz: A physics-data-driven framework for Black-Box and Gray-Box identification of chaotic systems with symbolic regression
7. Red blood cell passage through deformable interendothelial slits in the spleen: Insights into splenic filtration and hemodynamics
8. SMS: Spiking marching scheme for efficient long time integration of differential equations
9. Bridging scales in multiscale bubble growth dynamics with correlated fluctuations using neural operator learning
10. A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks
11. GMC-PINNs: A new general Monte Carlo PINNs method for solving fractional partial differential equations on irregular domains
12. NeuroSEM: A hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements
13. Uncertainty quantification for noisy inputs–outputs in physics-informed neural networks and neural operators
14. On the geometry transferability of the hybrid iterative numerical solver for differential equations
15. Peripheral arterial pathology and osteoarthritis of the knee: US examination of arterial wall stiffness, thickness, and flow characteristics
16. Tackling the curse of dimensionality in fractional and tempered fractional PDEs with physics-informed neural networks
17. Learning thermoacoustic interactions in combustors using a physics-informed neural network
18. Learning characteristic parameters and dynamics of centrifugal pumps under multiphase flow using physics-informed neural networks
19. ViTO: Vision Transformer-Operator
20. Tackling the curse of dimensionality with physics-informed neural networks
21. RiemannONets: Interpretable neural operators for Riemann problems
22. Physics-informed neural networks for predicting gas flow dynamics and unknown parameters in diesel engines
23. Two-stage initial-value iterative physics-informed neural networks for simulating solitary waves of nonlinear wave equations
24. Correcting model misspecification in physics-informed neural networks (PINNs)
25. Two-component macrophage model for active phagocytosis with pseudopod formation
26. Hutchinson Trace Estimation for high-dimensional and high-order Physics-Informed Neural Networks
27. TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers
28. Residual-based attention in physics-informed neural networks
29. Deep neural operators as accurate surrogates for shape optimization
30. En-DeepONet: An enrichment approach for enhancing the expressivity of neural operators with applications to seismology
31. Learning stiff chemical kinetics using extended deep neural operators
32. Discovering a reaction–diffusion model for Alzheimer’s disease by combining PINNs with symbolic regression
33. Deep neural operators can predict the real-time response of floating offshore structures under irregular waves
34. A generative modeling framework for inferring families of biomechanical constitutive laws in data-sparse regimes
35. Multifidelity deep operator networks for data-driven and physics-informed problems
36. A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions
37. Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology
38. A hybrid deep neural operator/finite element method for ice-sheet modeling
39. A framework based on symbolic regression coupled with eXtended Physics-Informed Neural Networks for gray-box learning of equations of motion from data
40. High-order methods for hypersonic flows with strong shocks and real chemistry
41. Deep transfer operator learning for partial differential equations under conditional shift
42. Discovering and forecasting extreme events via active learning in neural operators
43. Circulating cellular clusters are associated with thrombotic complications and clinical outcomes in COVID-19
44. Reliable extrapolation of deep neural operators informed by physics or sparse observations
45. In silico and in vitro study of the adhesion dynamics of erythrophagocytosis in sickle cell disease
46. On the influence of over-parameterization in manifold based surrogates and deep neural operators
47. Accelerating gradient descent and Adam via fractional gradients
48. Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons
49. Neural operator prediction of linear instability waves in high-speed boundary layers
50. Instability-wave prediction in hypersonic boundary layers with physics-informed neural operators
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