7 papers
AutoMOOSE: Use Case and Logical Views of Agentic Phase-Field Simulation Software
Sukriti Manna, Henry Chan, Subramanian Sankaranarayanan
AutoMOOSE is an agentic software framework that converts a natural-language request into an executed, screened, and interpreted MOOSE phase-field simulation. Here, we deploy AutoMO…
AutoMOOSE: An Agentic AI for Autonomous Phase-Field Simulation
Sukriti Manna, Henry Chan, Subramanian K. R. S. Sankaranarayanan
Phase-field modeling links thermodynamics and kinetics to microstructural evolution, but multiphysics frameworks such as MOOSE require expertise to construct inputs, manage campaig…
Physics-Informed Tree Search for High-Dimensional Computational Design
Suvo Banik, Troy D. Loeffler, Henry Chan +4
High-dimensional design spaces underpin a wide range of physics-based modeling and computational design tasks in science and engineering. These problems are commonly formulated as…
Physically Interpretable Interatomic Potentials via Symbolic Regression and Reinforcement Learning
Bilvin Varughese, Troy D. Loeffler, Suvo Banik +9
The development of next-generation molecular simulation models requires moving beyond pre-defined functional forms toward machine learning (ML) techniques that directly capture mul…
Interface and Thermophysical Properties of R32 Refrigerant
Abibat Adekoya-Olowofela, Sukriti Manna, Subramanian KRS Sankaranarayanan
Driven by the urgent demand for efficient cooling in microelectronics and advanced thermal management systems, difluoromethane (R32/CH2F2) has emerged as a promising candidate owin…
Comparison of Deterministic and Probabilistic Machine Learning Algorithms for Precise Dimensional Control and Uncertainty Quantification in Additive Manufacturing
Dipayan Sanpui, Anirban Chandra, Henry Chan +2
We present a probabilistic framework to accurately estimate dimensions of additively manufactured components. Using a dataset of 405 parts from nine production runs involving two m…