Recent Works

NeuroSEM: A hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements

Uncertainty quantification for noisy inputs–outputs in physics-informed neural networks and neural operators

Leveraging viscous Hamilton–Jacobi PDEs for uncertainty quantification in scientific machine learning

Learning thermoacoustic interactions in combustors using a physics-informed neural network

Learning characteristic parameters and dynamics of centrifugal pumps under multiphase flow using physics-informed neural networks

Neural operator learning for multiscale bubble growth dynamics with correlated fluctuations

Kolmogorov Artificial Intelligence Velocimetry infers hidden temperature from turbulent experimental velocity data

Convolutional feature-enhanced physics-informed neural networks for the spatio-temporal reconstruction of two-phase flows

A high fidelity numerical study on the vortex induced vibration of a rigid cylinder in 10³<Re_D<10⁶

Modelling cerebrospinal fluid dynamics in a mouse brain

Randomized Forward Mode Gradient for Spiking Neural Networks in Scientific Machine Learning

Bridging scales in multiscale bubble growth dynamics with correlated fluctuations using neural operator learning

Red blood cell passage through deformable interendothelial slits in the spleen: Insights into splenic filtration and hemodynamics

AI-Lorenz: A physics-data-driven framework for black-box and gray-box identification of chaotic systems with symbolic regression

Sms: Spiking marching scheme for efficient long time integration of differential equations

Peripheral arterial pathology and osteoarthritis of the knee: US examination of arterial wall stiffness, thickness, and flow characteristics

Biomechanics of phagocytosis of red blood cells by macrophages in the human spleen

From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning

Physics-informed computer vision: A review and perspectives

SympGNNs: Symplectic Graph Neural Networks for identifiying high-dimensional Hamiltonian systems and node classification

Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology

System for generating electricity from an underwater ocean stream

A time-dependent symplectic network for non-convex path planning problems with linear and nonlinear dynamics

Multiscale modeling framework of a constrained fluid with complex boundaries using twin neural networks

Synergistic learning with multi-task deeponet for efficient pde problem solving

Tackling the curse of dimensionality with physics-informed neural networks

Vito: Vision transformer-operator

Inferring turbulent velocity and temperature fields and their statistics from Lagrangian velocity measurements using physics-informed Kolmogorov-Arnold Networks

Rethinking materials simulations: Blending direct numerical simulations with neural operators

Neural operator learning for long-time integration in dynamical systems with recurrent neural networks

Score-fPINN: Fractional Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker-Planck-Levy Equations

Tackling the Curse of Dimensionality in Fractional and Tempered Fractional PDEs with Physics-Informed Neural Networks

Two-level overlapping additive Schwarz preconditioner for training scientific machine learning applications

Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems

Leveraging Hamilton-Jacobi PDEs with time-dependent Hamiltonians for continual scientific machine learning

A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks

Laplace neural operator for solving differential equations

RiemannONets: Interpretable neural operators for Riemann problems

Transformers as Neural Operators for Solutions of Differential Equations with Finite Regularity

Deep operator learning-based surrogate models for aerothermodynamic analysis of AEDC hypersonic waverider

Theoretical foundations of physics-informed neural networks and deep neural operators: A brief review

Large scale scattering using fast solvers based on neural operators

Predicting velocimetry using machine learning models

Two-stage initial-value iterative physics-informed neural networks for simulating solitary waves of nonlinear wave equations

Correcting model misspecification in physics-informed neural networks (PINNs)

Physics-Informed Neural Networks Enhanced Particle Tracking Velocimetry: An Example for Turbulent Jet Flow

Two-component macrophage model for active phagocytosis with pseudopod formation

Hutchinson trace estimation for high-dimensional and high-order physics-informed neural networks

GMC-PINNs: A new general Monte Carlo PINNs method for solving fractional partial differential equations on irregular domains

Leveraging multitime Hamilton–Jacobi PDEs for certain scientific machine learning problems

FIRSTLING-DIGIMAR, a Pilot Scale Digital Twin of a Marine Riser for Field Use

Spiking Physics-Informed Neural Networks on Loihi 2

Enhancing training of physics-informed neural networks using domain decomposition–based preconditioning strategies

ChatGPT-Enhanced ROC Analysis (CERA): A shiny web tool for finding optimal cutoff points in biomarker analysis

Tensor neural networks for high-dimensional Fokker-Planck equations

Characterization of partial wetting by CMAS droplets using multiphase many-body dissipative particle dynamics and data-driven discovery based on PINNs

TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers

Learning in PINNs: Phase transition, total diffusion, and generalization

AI-Aristotle: A physics-informed framework for systems biology gray-box identification

4.1. Developing a signaling-biophysical integrated systems biology model of red blood cell phagocytosis in sickle cell disease

Residual-based attention in physics-informed neural networks

Deep neural operators as accurate surrogates for shape optimization

Two-scale Neural Networks for Partial Differential Equations with Small Parameters

En-DeepONet: An enrichment approach for enhancing the expressivity of neural operators with applications to seismology

Rethinking skip connections in Spiking Neural Networks with Time-To-First-Spike coding

Score-based physics-informed neural networks for high-dimensional Fokker-Planck equations

Signaling-biophysical modeling unravels mechanistic control of red blood cell phagocytosis by macrophages in sickle cell disease

Discovering a reaction–diffusion model for Alzheimer’s disease by combining PINNs with symbolic regression

Learning stiff chemical kinetics using extended deep neural operators

Deep neural operators can predict the real-time response of floating offshore structures under irregular waves

Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operators

Deeponet based preconditioning strategies for solving parametric linear systems of equations

Inferring in vivo murine cerebrospinal fluid flow using artificial intelligence velocimetry with moving boundaries and uncertainty quantification

Multi-Modality Deep Infarct: Non-invasive identification of infarcted myocardium using composite in-silico-human data learning

NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators