arXiv subjects

Hoang Anh Nguyen

Publications and source records attributed to Hoang Anh Nguyen.

6 recordsLinked to original sources

arXiv2026

Convolution absorbing boundaries for explicit-circuit quantum simulation of the wave equation

Explicit quantum circuits for the wave equation, built by Hamiltonian simulation, are restricted to closed domains, in which outgoing waves reflect off the edge of the computational region and return. We lift that restriction with absorbing boundaries of the convolution type - a complex-frequency-shifted perfectly matched layer realised through exponential-kernel memory variables - and make the resulting non-unitary dynamics quantum-implementable by Schrodingerisation. We obtain three results. First, explicit gate-level circuits for the absorbing evolution, obtained by extending a Bell- basis term-evolution circuit to projector-valued operator strings and Trotterising to second order; these run end to end on seventeen to twenty-one qubits and agree with exact references to within a tenth of a percent to a percent. Second, a structural obstruction: the memory form of the absorbing generator carries an irreducibly indefinite Hermitian part, whose largest eigenvalue grows as the square root of the absorption strength divided by the grid spacing and survives any diagonal rescaling of the memory fields. The known recovery threshold for Schrodingerisation then makes the post-selection cost grow exponentially in the simulated time. Third, a remedy: a Lyapunov symmetrizer, precomputed classically, renders the transformed generator dissipative and replaces that time-dependent penalty with a time-independent conditioning factor of several hundred, measured on grids of up to four thousand unknowns and saturating under mesh refinement. The crossover is early: past it the symmetrized recovery is cheaper by four to twenty-six orders of magnitude in post-selection cost, and at the longest horizons tested it is the only recovery that works.

quant-ph↗

arXiv2026

Adversarial Agents on Topology Optimization: Understanding the Fragility and Robustness of Deep Learning-based and Physics-Based Design Models under Adversarial Perturbation

Topology optimization, using both physic-based approaches and deep learning surrogates, serves as a cornerstone for generative design agents in cyber-manufacturing systems. While deep learning surrogates have gained widespread adoption due to their speed in online design generation, this work demonstrates their vulnerability under input perturbations. In this work, we present a mechanics-grounded reliability evaluation framework that formulates an adversarial agent targeting the generative design models. We investigate a strictly non-intrusive threat model where bounded perturbations are introduced exclusively to the initial-density channel, while physical boundary conditions, compliance-gradient channels, network architectures, and solver routines remain intact. Evaluating surrogate models across U-Net, convolutional, and generative architectures with varying physics-gradient conditioning depths demonstrates that bounded initialization noise can cause catastrophic mechanical failure, increasing compliance by multiple orders of magnitude through severed load paths and disconnected supports. Furthermore, we discover that incorporating richer physics-gradient conditioning in the deep learning surrogates does not guarantee monotonic robustness across surrogate families. Finally, physics-in-the-loop recovery demonstrates that initializing the classical SIMP optimizer with perturbed topologies mitigates design performance degradation, having a high probability of restoring compliance to near-baseline levels across tested instances. These findings demonstrate that learned surrogates should serve as physics-verified initializers instead of replacing physics-based solvers entirely in a resilient cyber-manufacturing system. Moreover, the proposed adversarial agent provides a foundation for future training generative design agents robust against noise and targeted perturbations.

cs.LG↗

arXiv2026

Joint elastic full waveform inversion of multi-component geophone and distributed acoustic sensing data

Joint full waveform inversion (FWI) of distributed acoustic sensing (DAS) and ocean-bottom node (OBN) data typically requires converting measured strain to particle velocity, introducing numerical noise and spectral distortion. To eliminate this, we present an elastic multi-parameter FWI framework using a velocity-stress-strain (VSS) formulation that directly models pressure, particle velocity, and gauge-length-averaged DAS strain from a single forward simulation. Data residuals are injected additively into a single backward simulation, making computational cost independent of the active sensor subsets. We benchmark individual and combined datasets on cross-talk and elastic Marmousi models. Our results show that joint inversion recovers elastic parameters more accurately than single deployments when the sensors offer complementary information. Specifically, pairing two-component geophones with a deviated borehole DAS cable yields the most accurate parameter recovery and mitigates inter-parameter cross-talk by providing a distinct physical observable and complementary depth aperture. We release our implementation as xFWI, an open-source, Devito-based Python package for scalable, multi-deployment inversions.

physics.geo-ph↗

arXiv2026

Accelerating physics-informed neural networks for full waveform inversion using a hybrid quantum-classical finite-basis architecture

Full waveform inversion (FWI) reconstructs heterogeneous material properties from receiver data but remains computationally demanding. Physics-informed neural networks (PINNs) and their domain-decomposed variants (FBPINNs) offer a mesh-free alternative but face convergence challenges when representing complex velocity fields. We present a hybrid quantum-classical FBPINN for acoustic FWI, bringing together quantum computing and classical machine learning, in which the decomposed wavefield network and the global velocity network are implemented as classical-to-quantum pipelines terminating in parameterized quantum circuits (PQCs). The PQCs are realized as differentiable JAX statevector simulators, enabling end-to-end automatic differentiation through the classical PINN, the quantum circuit, and the physics-informed loss. On a geophysical anomaly benchmark, the quantum hybrid reaches a lower L1 velocity error than the primary classical FBPINN baseline in approximately 8x fewer training iterations, despite using approximately 33% fewer trainable parameters, and it outperforms all 15 classical hyperparameter variants tested. A second benchmark (checkerboard) demonstrates the generality of the inversion pipeline, confirming that the quantum hybrid architecture can recover structured spatial variations beyond the localized anomaly benchmark. Our framework is broadly applicable to wave-based inverse problems beyond geophysics, including medical ultrasound tomography and non-destructive evaluation.

physics.geo-ph↗

arXiv2026

MCERF: Advancing Multimodal LLM Evaluation of Engineering Documentation with Enhanced Retrieval

Engineering rulebooks and technical standards contain multimodal information like dense text, tables, and illustrations that are challenging for retrieval augmented generation (RAG) systems. Building upon the DesignQA framework [1], which relied on full-text ingestion and text-based retrieval, this work establishes a Multimodal ColPali Enhanced Retrieval and Reasoning Framework (MCERF), a system that couples a multimodal retriever with large language model reasoning for accurate and efficient question answering from engineering documents. The system employs the ColPali, which retrieves both textual and visual information, and multiple retrieval and reasoning strategies: (i) Hybrid Lookup mode for explicit rule mentions, (ii) Vision to Text fusion for figure and table guided queries, (iii) High Reasoning LLM mode for complex multi modal questions, and (iv) SelfConsistency decision to stabilize responses. The modular framework design provides a reusable template for future multimodal systems regardless of underlying model architecture. Furthermore, this work establishes and compares two routing approaches: a single case routing approach and a multi-agent system, both of which dynamically allocate queries to optimal pipelines. Evaluation on the DesignQA benchmark illustrates that this system improves average accuracy across all tasks with a relative gain of +41.1% from baseline RAG best results, which is a significant improvement in multimodal and reasoning-intensive tasks without complete rulebook ingestion. This shows how vision language retrieval, modular reasoning, and adaptive routing enable scalable document comprehension in engineering use cases.

cs.IR↗

arXiv2025

Seismic Traveltime Inversion with Quantum Annealing

This study demonstrates the application of quantum computing based quantum annealing to seismic traveltime inversion, a critical approach for inverting highly accurate velocity models. The seismic inversion problem is first converted into a Quadratic Unconstrained Binary Optimization problem, which the quantum annealer is specifically designed to solve. We then solve the problem via quantum annealing method. The inversion is applied on a synthetic velocity model, presenting a carbon storage scenario at depths of 1000-1300 meters. As an application example, we also show the capacity of quantum computing to handle complex, noisy data environments. This work highlights the emerging potential of quantum computing in geophysical applications, providing a foundation for future developments in high-precision seismic imaging.

physics.geo-ph↗