Box 1860
Providence, RI 02912
United States
Brown University
SSRN RANKINGS
in Total Papers Citations
Neural Operator, State-Space Model, Mamba, Spatio-Temporal PDE
Sparse and noisy data, PINNs, functional priors, uncertainty quantification, normalizing flows, Bayesian inference
Caputo fractional derivative, Non-local calculus, optimization, Adam, Neural Networks
Residual-based attention, PINNs accuracy, adaptive weights.
Scientific Machine Learning, wave scattering, Helmholtz equation, neural operator, Krylov methods, DeepONet
Quasi-Newton Methods, Second-Order Optimization, Kolmogorov-Arnold Networks (PIKANs), Physics-Informed Neural Networks (PINNs)
Operator neural networks, Phase-field approach, Physics-informed methodologies, Data-driven modeling, Crack propagation, Kolmogorov-Arnold Network (KAN)
Scientific machine learning, wave scattering, Helmholtz equation, neural operator, DeepONet
Scientific machine learning, Kolmogorov-Arnold networks, Physics-informed neural networks, operator networks, partial differential equations
model uncertainty, Physics-Informed Neural Networks, model misspecification, Uncertainty Quantification, non-Newtonian flows, symbolic regression
Physics-Informed Neural Networks (PINNs), causality, domain decomposition, transfer learning
Stiff problems, Scientific machine learning, Neural operators, Combustion
deep learning, Graph neural networks, Physics-informed, Hamiltonian systems, Symplectic maps, node classification
Neural operators, DeepONet, Extrapolation complexity, Fine-tuning, Multifidelity learning, Out-of-distribution inference
Neural Operator, State-Space Model, Mamba, dynamical system
Physics-Informed Neural Networks, Curse of Dimensionality
uncertainty quantification, Bayesian inference, noisy inputs-outputs, PINNs, neural operators, synergistic learning
operator learning, Preconditioning, Hybridization, Spectral bias, Numerical optimization, Pareto optimality
Crack PropagationDiscrete Particle SystemsDeep Operator Networks (DeepONets)Constitutively Informed Particle Dynamics (CPD)Fracture PredictionFusion DeepONet
PET bottle buckling, Transolver, DeepONet, computational mechanics, scientific machine learning
Deformable interendothelial slits spleen Filtration Dissipartive particle dynamics SSickle cell disease
Graph Embedding, Transformer, dynamic graphs, Link prediction, unsupervised contrastive learning, long-term dependencies
Constitutive Relation, Uncertainty Quantification, Functional Prior, Generative Adversarial Network, Deep Operator Network, Bayesian Inference
Neural Operator, UNet, Inception UNet (IUNet), Multi-scale Multi-branch (M&M), Earth System Modeling, Bias Correction
Multiscale Modeling, multiphase flow, Deep Learning, Dissipative Particle Dynamics, Rayleigh-Plesset Equation
structure-preserving, physics-informed neural networks, Gray-box discovery, Functional derivatives, Non-equilibrium thermodynamics
physics-informed machine learning, PINNs, spectral element method, data assimilation, multiphysics problems, heat transfer
Physics-informed neural networks, Curse of Dimensionality, Fractional and Tempered Fractional PDEs, High-Dimensional PDEs
Milli-spinner, Blood clot, Clot debulking, Fibrin densification, Clot volume reduction
