Postdoctoral researcher at GIST.
I develop scientific AI frameworks that go beyond prediction to become tools for
physical discovery. My work focuses on inferring hidden states, unresolved mechanisms,
and missing closure relations in complex transport systems from sparse observations,
governing equations, mechanistic models, and physics-informed learning.
My research background is rooted in phase-change heat transfer and multiphase transport,
with additional experience in chaotic advection, transport enhancement, and thermal management systems.
I aim to build interpretable, AI-augmented discovery pipelines that reveal the underlying physics
rather than merely fitting data.
I develop scientific AI methods to discover hidden physics and missing closure relations in complex transport systems. Rather than treating AI as a black-box predictor, I focus on frameworks that infer latent mechanisms and governing relations by integrating governing equations, sparse measurements, mechanistic models, neural operators, and physics-informed learning.
Many transport systems are governed by equations that are only partially known.
Unresolved mechanisms, latent variables, and empirical closures create fundamental
barriers to predictive understanding. I study how scientific AI can move beyond
forward modeling to become a discovery instrument — one that can infer hidden states,
identify missing closure relations, and produce interpretable models even when observations
are sparse and incomplete.
This direction targets regimes where traditional methods struggle: systems with strong
nonlinearity, multiscale interactions, interfacial dynamics, and limited observability.
Phase-change and multiphase systems offer a uniquely challenging testbed for
hidden-physics discovery. Key mechanisms — nucleation-site activation, bubble growth,
microlayer evaporation, interfacial transport, heat-flux partitioning, and flow instabilities —
are often only partially observable even in well-instrumented experiments.
My background includes subcooled flow boiling experiments, density-wave oscillation studies,
CHF characterization, and mechanistic modeling in annular and microchannel geometries.
I am extending this foundation toward physics-informed inverse modeling, neural operators,
and closure discovery for unresolved interfacial transport.
Thermal transport systems connect heat generation, fluid transport, material response,
and performance constraints across multiple scales. These systems often require hidden-state
inference because internal heat sources, effective boundary conditions, thermal distortion,
and transport coefficients cannot be measured directly.
My recent work has involved high-power laser thermal management, liquid cooling of gain media,
heat-load estimation, thermo-optic distortion, and coupled thermal-fluid analysis. I view these
systems as emerging applications for scientific AI, inverse heat transfer, and operator-learning
surrogates for design and diagnostics.
Chaotic advection and laminar mixing provide clean, interpretable settings for studying
hidden flow structures and transport enhancement. Stretching and folding dynamics,
transport barriers, scalar dispersion, and effective diffusivity can be analyzed through
CFD, Poincaré sections, dynamical-systems concepts, and scientific machine learning.
My prior numerical work on groove-embedded and barrier-embedded pipe mixers established
a foundation in chaotic transport. I use this background as a supporting application area
for hidden-structure discovery and data-driven modeling of nonlinear transport phenomena.
Selected works tracing the trajectory from chaotic transport and multiphase experiments to mechanistic modeling and physics-informed inverse problems.
Research trajectory spanning thermofluid experiments, mechanistic modeling, computational transport, thermal systems, and scientific machine learning.
I welcome enquiries on hidden physics discovery with AI, closure learning, inverse problems in transport systems, phase-change phenomena, thermal transport, and chaotic mixing.