7 citations · 7 across the 4 of their papers we have counts for
5 papers
Torch-DPhys: A Torch-Native, MOOSE-Compatible Framework for Differentiable Physics Simulation and Hybrid Machine Learning Integration
Akzhol Almukhametov, Yang Liu
We present Torch-DPhys, a Torch-native framework implemented in PyTorch for differentiable physics simulation and hybrid physics-machine learning. It implements selected thermal-hy…
Deploying Frontier Agentic Technology in MOOSEnger, a Multiphysics-Capable AI Assistant
Zaid Abulawi, Mengnan Li, Guillaume Giudicelli +2
The Multiphysics Object-Oriented Simulation Environment (MOOSE) is an open-source finite-element framework for building multiphysics simulation applications. Using a multiphysics e…
Graph Neural ODE Digital Twins for Control-Oriented Reactor Thermal-Hydraulic Forecasting Under Partial Observability
Akzhol Almukhametov, Doyeong Lim, Rui Hu +1
Real-time supervisory control of advanced reactors requires accurate forecasting of plant-wide thermal-hydraulic states, including locations where physical sensors are unavailable.…
Automating Data-Driven Modeling and Analysis for Engineering Applications using Large Language Model Agents
Yang Liu, Zaid Abulawi, Abhiram Garimidi +1
Modern engineering increasingly relies on vast datasets generated by experiments and simulations, driving a growing demand for efficient, reliable, and broadly applicable modeling…
An AI-Driven Thermal-Fluid Testbed for Advanced Small Modular Reactors: Integration of Digital Twin and Large Language Models
Doyeong Lim, Yang Liu, Zavier Ndum Ndum +2
This paper presents a multipurpose artificial intelligence (AI)-driven thermal-fluid testbed designed to advance Small Modular Reactor technologies by seamlessly integrating physic…