8 papers · 1 filter
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…
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…
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…
HUG-VAS: A Hierarchical NURBS-Based Generative Model for Aortic Geometry Synthesis and Controllable Editing
Pan Du, Mingqi Xu, Xiaozhi Zhu +1
Accurate, patient-specific vascular geometry is pivotal for diagnosis, planning, and device design, yet existing statistical shape modeling (SSM) pipelines rely on linear priors an…
Implicit Neural Differential Model for Spatiotemporal Dynamics
Deepak Akhare, Pan Du, Tengfei Luo +1
Hybrid neural-physics modeling frameworks through differentiable programming have emerged as powerful tools in scientific machine learning, enabling the integration of known physic…
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…
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…
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…