Most physical systems cannot be fully observed. Their performance and failure are governed by
hidden physical states such as temperature fields, stress distributions, and interfacial dynamics.
My work develops AI models that infer these hidden states from limited observations, enabling real-time prediction of performance, failure risk, and safe operating limits.
The goal is to make complex physical systems understandable, predictable, and controllable in real time.
This research program is organized around one question: how can hidden physical states be inferred from partial observations, and used to predict performance, failure, and safe operating limits in thermo-fluid systems?
Most engineering systems are only partially observable. However, critical outcomes—performance degradation, fracture, and burnout—are governed by internal states that cannot be directly measured. My work aims to infer these hidden states using physics-informed learning, inverse modeling, and operator-based methods.
Experimental and numerical investigation of subcooled and saturated flow boiling in microchannels, concentric annuli, and finned geometries. Current focus includes onset of nucleate boiling, bubble dynamics, and critical heat flux in phase-change systems.
Thermo-optical analysis of liquid-immersion-cooled solid-state laser systems under high heat flux. Focus on hidden states such as internal temperature fields, thermal stress, refractive index variation, and their effects on beam quality degradation, safe operating limits, and fracture risk.
Ongoing work develops physics-informed neural operators and inverse learning frameworks to predict hidden states, performance, and failure conditions from sparse observations. The long-term goal is real-time physical decision making for design, operation, and control.
Selected publications in thermal-fluid dynamics and physical systems modeling. Physics-informed AI work is currently in preparation.
I welcome enquiries regarding research collaboration, scientific machine learning for physical systems, or HiddenField pilot partnerships.