199 citations · 302 across the 61 of their papers we have counts for
9 papers · 1 filter
Pharmacometrics Modeling via Physics-Informed Neural Networks: Integrating Time-Variant Absorption Rates and Fractional Calculus for Enhancing Prediction Accuracy
Nazanin Ahmadi, Shupeng Wang, George Karniadakis
We present a novel method to improve pharmacokinetics modeling, an essential step of drug development. Conventional models frequently fail to fully represent the intricacies of dru…
Automatic discovery of optimal meta-solvers via multi-objective optimization
Youngkyu Lee, Shanqing Liu, Jerome Darbon +1
We design two classes of ultra-fast meta-solvers for linear systems arising after discretizing PDEs by combining neural operators with either simple iterative solvers, e.g., Jacobi…
Randomized Forward Mode Gradient for Spiking Neural Networks in Scientific Machine Learning
Ruyin Wan, Qian Zhang, George Em Karniadakis
Spiking neural networks (SNNs) represent a promising approach in machine learning, combining the hierarchical learning capabilities of deep neural networks with the energy efficien…
PINNs4Drops: Video-conditioned physics-informed neural networks for two-phase flow reconstruction
Maximilian Dreisbach, Elham Kiyani, Jochen Kriegseis +2
Two-phase flow phenomena underpin critical technologies such as hydrogen fuel cells, spray cooling, and combustion, where droplet dynamics govern performance and efficiency. Conven…
From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning
Juan Diego Toscano, Vivek Oommen, Alan John Varghese +4
Physics-Informed Neural Networks (PINNs) have emerged as a key tool in Scientific Machine Learning since their introduction in 2017, enabling the efficient solution of ordinary and…
CMINNs: Compartment Model Informed Neural Networks -- Unlocking Drug Dynamics
Nazanin Ahmadi Daryakenari, Shupeng Wang, George Karniadakis
In the field of pharmacokinetics and pharmacodynamics (PKPD) modeling, which plays a pivotal role in the drug development process, traditional models frequently encounter difficult…
Integrating Neural Operators with Diffusion Models Improves Spectral Representation in Turbulence Modeling
Vivek Oommen, Aniruddha Bora, Zhen Zhang +1
We integrate neural operators with diffusion models to address the spectral limitations of neural operators in surrogate modeling of turbulent flows. While neural operators offer c…
SympGNNs: Symplectic Graph Neural Networks for identifiying high-dimensional Hamiltonian systems and node classification
Alan John Varghese, Zhen Zhang, George Em Karniadakis
Existing neural network models to learn Hamiltonian systems, such as SympNets, although accurate in low-dimensions, struggle to learn the correct dynamics for high-dimensional many…
A time-dependent symplectic network for non-convex path planning problems with linear and nonlinear dynamics
Zhen Zhang, Chenye Wang, Shanqing Liu +2
We propose a novel neural network architecture (TSympOCNet) to address high--dimensional optimal control problems with linear and nonlinear dynamics. An important application of th…