8 papers
Process-Informed Forecasting of Complex Thermal Dynamics in Pharmaceutical Manufacturing
Ramona Rubini, Siavash Khodakarami, Aniruddha Bora +2
Accurate time-series forecasting for complex physical systems is the backbone of modern industrial monitoring and control, yet deep learning models often lack the physical consiste…
Physics-informed offline reinforcement learning eliminates catastrophic fuel waste in maritime routing
Aniruddha Bora, Julie Chalfant, Chryssostomos Chryssostomidis
International shipping produces approximately 3% of global greenhouse gas emissions, yet voyage routing remains dominated by heuristic methods. We present PIER (Physics-Informed, E…
Enhancing Heat Sink Efficiency in MOSFETs using Physics Informed Neural Networks: A Systematic Study on Coolant Velocity Estimation
Aniruddha Bora, Isabel K. Alvarez, Julie Chalfant +1
In this work, we present a methodology using Physics Informed Neural Networks (PINNs) to determine the required velocity of a coolant, given inlet and outlet temperatures for a giv…
Retrofitting Earth System Models with Cadence-Limited Neural Operator Updates
Aniruddha Bora, Shixuan Zhang, Khemraj Shukla +3
Coarse resolution, imperfect parameterizations, and uncertain initial states and forcings limit Earth-system model (ESM) predictions. Traditional bias correction via data assimilat…
Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction
Vivek Oommen, Siavash Khodakarami, Aniruddha Bora +2
Neural operators are promising surrogates for dynamical systems but when trained with standard L2 losses they tend to oversmooth fine-scale turbulent structures. Here, we show that…
Mitigating Spectral Bias in Neural Operators via High-Frequency Scaling for Physical Systems
Siavash Khodakarami, Vivek Oommen, Aniruddha Bora +1
Neural operators have emerged as powerful surrogates for modeling complex physical problems. However, they suffer from spectral bias making them oblivious to high-frequency modes,…