activity
20242026
collaborators

7 papers

physics.flu-dyn2025

Conditional neural field for spatial dimension reduction of turbulence data: a comparison study

Junyi Guo, Pan Du, Xiantao Fan +2

We investigate conditional neural fields (CNFs), mesh-agnostic, coordinate-based decoders conditioned on a low-dimensional latent, for spatial dimensionality reduction of turbulent…

physics.geo-ph2025

Generative Latent Diffusion Model for Inverse Modeling and Uncertainty Analysis in Geological Carbon Sequestration

Zhao Feng, Xin-Yang Liu, Meet Hemant Parikh +4

Geological Carbon Sequestration (GCS) has emerged as a promising strategy for mitigating global warming, yet its effectiveness heavily depends on accurately characterizing subsurfa…

cs.CV2025

AortaDiff: Volume-Guided Conditional Diffusion Models for Multi-Branch Aortic Surface Generation

Delin An, Pan Du, Jian-Xun Wang +1

Accurate 3D aortic construction is crucial for clinical diagnosis, preoperative planning, and computational fluid dynamics (CFD) simulations, as it enables the estimation of critic…

physics.comp-ph2025

JAX-BTE: A GPU-Accelerated Differentiable Solver for Phonon Boltzmann Transport Equations

Wenjie Shang, Jiahang Zhou, J. P. Panda +5

This paper introduces JAX-BTE, a GPU-accelerated, differentiable solver for the phonon Boltzmann Transport Equation (BTE) based on differentiable programming. JAX-BTE enables accur…

cs.CV2025

AI-Powered Automated Model Construction for Patient-Specific CFD Simulations of Aortic Flows

Pan Du, Delin An, Chaoli Wang +1

Image-based modeling is essential for understanding cardiovascular hemodynamics and advancing the diagnosis and treatment of cardiovascular diseases. Constructing patient-specific…

cs.CV2025

Hierarchical LoG Bayesian Neural Network for Enhanced Aorta Segmentation

Delin An, Pan Du, Pengfei Gu +2

Accurate segmentation of the aorta and its associated arch branches is crucial for diagnosing aortic diseases. While deep learning techniques have significantly improved aorta segm…