Yulong
Liu

Computational Geomechanics +
Scientific Machine Learning

Ph.D. candidate · Cornell University

Earth and Atmospheric Sciences · Cornell University

Minors in Computer Science and Scientific Computing · Advised by Chloé Arson

I work at the intersection of computational geomechanics, poromechanics, reservoir simulation, and scientific machine learning. My research combines coupled multiphysics simulation of subsurface and reservoir systems with physics-informed neural networks, operator learning, and PDE-grounded verification of LLM-generated simulation code.

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Research interests

Computational geomechanics

Coupled thermo-hydro-mechanical models of fractured rock, pressurized cavities, excavation, and reservoir systems using finite elements and MOOSE.

Scientific machine learning

Physics-informed neural networks, implicit neural representations, and operator learning for geometry-aware, physics-aware prediction.

Reservoirs & porous media

Poromechanics, geothermal injection-production, fracture-controlled transport, and multiscale upscaling in porous subsurface media.

Reliable AI for simulation

PDE-grounded verification and LLM-based systems that reason about physical intent, not just syntactically valid simulation code.

Full research overview

Selected publications

• A Physics-Informed Neural Network for Pressurized Cavities of Arbitrary Shape in Heterogeneous Rock

  Yulong Liu, Chloé Arson

  Rock Mechanics and Rock Engineering, 2026Journal

• Your Simulation Runs but Solves the Wrong Physics: PDE-Grounded Intent Verification for LLM-Generated Multiphysics Simulation Code

  Yulong Liu, Zhenghan Song, et al.

  arXiv preprint · under review at NeurIPS 2026Preprint

• Operator Learning Surrogate Modeling of Hydraulically Fractured Geothermal Injection-Production Systems: A Cornell Case Study

  Yulong Liu, Jonah Botvinick-Greenhouse, Yunan Yang, Chloé Arson

  ARMA 2026, TucsonConference

All publications