14 citations · 24 across the 6 of their papers we have counts for
7 papers
Curvature-Aware Optimization for High-Accuracy Physics-Informed Neural Networks
Anas Jnini, Elham Kiyani, Khemraj Shukla +5
Efficient and robust optimization is essential for neural networks, enabling scientific machine learning models to converge rapidly to very high accuracy -- faithfully capturing co…
Spectral bias in physics-informed and operator learning: Analysis and mitigation guidelines
Siavash Khodakarami, Vivek Oommen, Nazanin Ahmadi Daryakenari +2
Solving partial differential equations (PDEs) by neural networks as well as Kolmogorov-Arnold Networks (KANs), including physics-informed neural networks (PINNs), physics-informed…
Representation Meets Optimization: Training PINNs and PIKANs for Gray-Box Discovery in Systems Pharmacology
Nazanin Ahmadi Daryakenari, Khemraj Shukla, George Em Karniadakis
Physics-Informed Kolmogorov-Arnold Networks (PIKANs) are gaining attention as an effective counterpart to the original multilayer perceptron-based Physics-Informed Neural Networks…
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…
State-space models are accurate and efficient neural operators for dynamical systems
Zheyuan Hu, Nazanin Ahmadi Daryakenari, Qianli Shen +2
Physics-informed machine learning (PIML) has emerged as a promising alternative to classical methods for predicting dynamical systems, offering faster and more generalizable soluti…