arXiv subjects

Pi-Yueh Chuang

Publications and source records attributed to Pi-Yueh Chuang.

4 recordsLinked to original sources

arXiv2026

Event-Level QCD Inference Framework for Quark-Gluon Imaging

We introduce and demonstrate an event-level analysis framework for quark-gluon imaging. For a first application we use it for the inference of parton distribution functions from synthetic deep inelastic scattering data. This framework removes the need for unfolding of detector effects and the binning of events, and therefore eliminates two key sources of information loss. We contrast this event-level framework with the traditional histogram approach by performing a closure test for parton distribution functions from event data obtained from a known ground truth. In this study we assume a perfect detector, which makes unfolding straightforward. The elimination of binning in the event-level framework is demonstrated to have important benefits over the traditional histogram approach, and performs better in the closure test, particularly for a smaller number of events. For example, defining a mean-squared error distance metric, we find that the event-level framework performs around $35\%$ better than the traditional approach for a moderate number of events. The benefits of an event-level framework should increase for inference associated with 3D quark-gluon imaging, because these differential cross sections are of higher dimension and the comparative number of measured events is significantly reduced.

hep-ph↗

arXiv2025

Distributional Sensitivity Analysis: Enabling Differentiability in Sample-Based Inference

This work introduces a mathematical framework for estimating the space-parameter sensitivity of random samples in arbitrary dimensions. Such sensitivity effectively acts as gradients of random samples with respect to distributional parameters, which are essential in sample-based inverse problems in nuclear physics, such as inferring quantum correlation functions. We present two analytical formulae for sensitivity and gradient estimation. The first interprets sensitivity as the partial derivatives of the inverse mapping of 1-D conditional distributions. The second, suited for optimization methods that tolerate inexact gradients, applies a diagonal approximation that reduces computational cost with minimal accuracy loss. When closed forms are unavailable, four second-order numerical algorithms are provided to approximate both expressions. Verification and validation studies confirm the correctness of these algorithms and the effectiveness of the proposed formulae. A nuclear physics application demonstrates how the framework enables uncertainty quantification and parameter inference for quantum correlation functions. Unlike existing approaches, our method requires neither model fitting nor knowledge of sampling algorithms or high-dimensional integrals, making it suitable for black-box or simulation-based samplers. Moreover, it renders arbitrary sampling subroutines differentiable, facilitating integration into deep learning and automatic differentiation frameworks. Algorithmic details and open-source implementations are provided to ensure reproducibility and promote further development.

stat.ML↗

arXiv2023

Predictive Limitations of Physics-Informed Neural Networks in Vortex Shedding

The recent surge of interest in physics-informed neural network (PINN) methods has led to a wave of studies that attest to their potential for solving partial differential equations (PDEs) and predicting the dynamics of physical systems. However, the predictive limitations of PINNs have not been thoroughly investigated. We look at the flow around a 2D cylinder and find that data-free PINNs are unable to predict vortex shedding. Data-driven PINN exhibits vortex shedding only while the training data (from a traditional CFD solver) is available, but reverts to the steady state solution when the data flow stops. We conducted dynamic mode decomposition and analyze the Koopman modes in the solutions obtained with PINNs versus a traditional fluid solver (PetIBM). The distribution of the Koopman eigenvalues on the complex plane suggests that PINN is numerically dispersive and diffusive. The PINN method reverts to the steady solution possibly as a consequence of spectral bias. This case study reaises concerns about the ability of PINNs to predict flows with instabilities, specifically vortex shedding. Our computational study supports the need for more theoretical work to analyze the numerical properties of PINN methods. The results in this paper are transparent and reproducible, with all data and code available in public repositories and persistent archives; links are provided in the paper repository at \url{https://github.com/barbagroup/jcs_paper_pinn}, and a Reproducibility Statement within the paper.

cs.CE↗

arXiv2022

Experience report of physics-informed neural networks in fluid simulations: pitfalls and frustration

Though PINNs (physics-informed neural networks) are now deemed as a complement to traditional CFD (computational fluid dynamics) solvers rather than a replacement, their ability to solve the Navier-Stokes equations without given data is still of great interest. This report presents our not-so-successful experiments of solving the Navier-Stokes equations with PINN as a replacement for traditional solvers. We aim to, with our experiments, prepare readers for the challenges they may face if they are interested in data-free PINN. In this work, we used two standard flow problems: 2D Taylor-Green vortex at Re=100 and 2D cylinder flow at Re=200. The PINN method solved the 2D Taylor-Green vortex problem with acceptable results, and we used this flow as an accuracy and performance benchmark. About 32 hours of training were required for the PINN method's accuracy to match the accuracy of a 16x16 finite-difference simulation, which took less than 20 seconds. The 2D cylinder flow, on the other hand, did not produce a physical solution. The PINN method behaved like a steady-flow solver and did not capture the vortex shedding phenomenon. By sharing our experience, we would like to emphasize that the PINN method is still a work-in-progress, especially in terms of solving flow problems without any given data. More work is needed to make PINN feasible for real-world problems in such applications.

physics.flu-dyn↗