arXiv subjects

Fong Yew Leong

Publications and source records attributed to Fong Yew Leong.

7 recordsLinked to original sources

arXiv2025

Differentiable Physics-Neural Models enable Learning of Non-Markovian Closures for Accelerated Coarse-Grained Physics Simulations

Numerical simulations provide key insights into many physical, real-world problems. However, while these simulations are solved on a full 3D domain, most analysis only require a reduced set of metrics (e.g. plane-level concentrations). This work presents a hybrid physics-neural model that predicts scalar transport in a complex domain orders of magnitude faster than the 3D simulation (from hours to less than 1 min). This end-to-end differentiable framework jointly learns the physical model parameterization (i.e. orthotropic diffusivity) and a non-Markovian neural closure model to capture unresolved, 'coarse-grained' effects, thereby enabling stable, long time horizon rollouts. This proposed model is data-efficient (learning with 26 training data), and can be flexibly extended to an out-of-distribution scenario (with a moving source), achieving a Spearman correlation coefficient of 0.96 at the final simulation time. Overall results show that this differentiable physics-neural framework enables fast, accurate, and generalizable coarse-grained surrogates for physical phenomena.

physics.comp-ph↗

arXiv2025

CompARE: A Computational framework for Airborne Respiratory disease Evaluation integrating flow physics and human behavior

The risk of indoor airborne transmission among co-located individuals is generally non-uniform, which remains a critical challenge for public health modelling. Thus, we present CompARE, an integrated risk assessment framework for indoor airborne disease transmission that reveals a striking bimodal distribution of infection risk driven by airflow dynamics and human behavior. Combining computational fluid dynamics (CFD), machine learning (ML), and agent-based modeling (ABM), our model captures the complex interplay between aerosol transport, human mobility, and environmental context. Based on a prototypical childcare center, our approach quantifies how incorporation of ABM can unveil significantly different infection risk profiles across agents, with more than two-fold change in risk of infection between the individuals with the lowest and highest risks in more than 90% of cases, despite all individuals being in the same overall environment. We found that infection risk distributions can exhibit not only a striking bimodal pattern in certain activities but also exponential decay and fat-tailed behavior in others. Specifically, we identify low-risk modes arising from source containment, as well as high-risk tails from prolonged close contact. Our approach enables near-real-time scenario analysis and provides policy-relevant quantitative insights into how ventilation design, spatial layout, and social distancing policies can mitigate transmission risk. These findings challenge simple distance-based heuristics and support the design of targeted, evidence-based interventions in high-occupancy indoor settings.

physics.soc-ph↗

arXiv2025

Hybrid Quantum Physics-informed Neural Network: Towards Efficient Learning of High-speed Flows

This study benchmarks hybrid quantum physics-informed neural network (HQPINN) to model high-speed flows, compared against classical physics-informed neural networks (PINNs) and fully quantum neural networks (QNNs). The HQPINN architecture integrates a parameterized quantum circuit (PQC) with a classical neural network in parallel, trained via a physics-informed loss. Across harmonic, non-harmonic, and transonic benchmarks, HQPINNs demonstrate balanced performance, offering competitive accuracy and stability with reduced parameter cost. Quantum PINNs are highly efficient for harmonic problems achieving the lowest loss with minimal parameters due to their Fourier structure, but struggle to generalize in non-harmonic settings involving shocks and discontinuities. HQPINNs mitigate such artifacts, and with sufficient parameterization, can match the performance of classical models in more complex regimes. Although constrained by current quantum emulation costs and scalability, HQPINNs show promise as general-purpose solvers, offering parameter efficiency with robust fallback behavior, particularly suited for problems where the nature of the solution is not known a-priori.

physics.comp-ph↗

arXiv2024

Solving Fractional Differential Equations on a Quantum Computer: A Variational Approach

We introduce an efficient variational hybrid quantum-classical algorithm designed for solving Caputo time-fractional partial differential equations. Our method employs an iterable cost function incorporating a linear combination of overlap history states. The proposed algorithm is not only efficient in time complexity, but has lower memory costs compared to classical methods. Our results indicate that solution fidelity is insensitive to the fractional index and that gradient evaluation cost scales economically with the number of time steps. As a proof of concept, we apply our algorithm to solve a range of fractional partial differential equations commonly encountered in engineering applications, such as the sub-diffusion equation, the non-linear Burgers' equation and a coupled diffusive epidemic model. We assess quantum hardware performance under realistic noise conditions, further validating the practical utility of our algorithm.

quant-ph↗

arXiv2023

Variational Quantum Simulation of Partial Differential Equations: Applications in Colloidal Transport

We assess the use of variational quantum imaginary time evolution for solving partial differential equations. Our results demonstrate that real-amplitude ansaetze with full circular entangling layers lead to higher-fidelity solutions compared to those with partial or linear entangling layers. To efficiently encode impulse functions, we propose a graphical mapping technique for quantum states that often requires only a single bit-flip of a parametric gate. As a proof of concept, we simulate colloidal deposition on a planar wall by solving the Smoluchowski equation including the Derjaguin-Landau-Verwey-Overbeek (DLVO) potential energy. We find that over-parameterization is necessary to satisfy certain boundary conditions and that higher-order time-stepping can effectively reduce norm errors. Together, our work highlights the potential of variational quantum simulation for solving partial differential equations using near-term quantum devices.

quant-ph↗

arXiv2022

Variational Quantum Evolution Equation Solver

Variational quantum algorithms offer a promising new paradigm for solving partial differential equations on near-term quantum computers. Here, we propose a variational quantum algorithm for solving a general evolution equation through implicit time-stepping of the Laplacian operator. The use of encoded source states informed by preceding solution vectors results in faster convergence compared to random re-initialization. Through statevector simulations of the heat equation, we demonstrate how the time complexity of our algorithm scales with the ansatz volume for gradient estimation and how the time-to-solution scales with the diffusion parameter. Our proposed algorithm extends economically to higher-order time-stepping schemes, such as the Crank-Nicolson method. We present a semi-implicit scheme for solving systems of evolution equations with non-linear terms, such as the reaction-diffusion and the incompressible Navier-Stokes equations, and demonstrate its validity by proof-of-concept results.

quant-ph↗

arXiv2020

Airborne dispersion of droplets during coughing: a physical model of viral transmission

The Covid-19 pandemic has focused attention on airborne transmission of viruses. Using realistic air flow simulation, we model droplet dispersion from coughing and study the transmission risk related to SARS-CoV-2. Although most airborne droplets are 8-16 $μ$m in diameter, the droplets with the highest transmission potential are, in fact, 32-40 $μ$m. Use of face masks is therefore recommended for both personal and social protection. We found social distancing effective at reducing transmission potential across all droplet sizes. However, the presence of a human body 1 m away modifies the aerodynamics so that downstream droplet dispersion is enhanced, which has implications on safe distancing in queues. Based on median viral load, we found that an average of 0.55 viral copies is inhaled at 1 m distance per cough. Droplet evaporation results in significant reduction in droplet counts, but airborne transmission remains possible even under low humidity conditions.

physics.flu-dyn↗