91 citations · 213 across the 33 of their papers we have counts for
5 papers · 1 filter
AB-PINNs: Adaptive-Basis Physics-Informed Neural Networks for Residual-Driven Domain Decomposition
Jonah Botvinick-Greenhouse, Wael H. Ali, Mouhacine Benosman +1
We introduce adaptive-basis physics-informed neural networks (AB-PINNs), a novel approach to domain decomposition for training PINNs in which existing subdomains dynamically adapt…
Physics-Informed Neural Controlled Differential Equations for Scalable Long Horizon Multi-Agent Motion Forecasting
Shounak Sural, Charles Kekeh, Wenliang Liu +2
Long-horizon motion forecasting for multiple autonomous robots is challenging due to non-linear agent interactions, compounding prediction errors, and continuous-time evolution of…
Designing Psychometric Bias Measures for ChatBots: An Application to Racial Bias Measurement
Mouhacine Benosman
Artificial intelligence (AI), particularly in the form of large language models (LLMs) or chatbots, has become increasingly integrated into our daily lives. In the past five years,…
A Dual Ensemble Kalman Filter Approach to Robust Control of Nonlinear Systems: An Application to Partial Differential Equations
Anant A. Joshi, Saviz Mowlavi, Mouhacine Benosman
This paper considers the problem of data-driven robust control design for nonlinear systems, for instance, obtained when discretizing nonlinear partial differential equations (PDEs…
Multi-robot Path Planning and Scheduling via Model Predictive Optimal Transport (MPC-OT)
Usman A. Khan, Mouhacine Benosman, Wenliang Liu +2
In this paper, we propose a novel methodology for path planning and scheduling for multi-robot navigation that is based on optimal transport theory and model predictive control. We…