arXiv subjects

Christoforos Rekatsinas

Publications and source records attributed to Christoforos Rekatsinas.

5 recordsLinked to original sources

arXiv2026

Probabilistic Physics-Informed Neural Solvers for Woods-Saxon Parameter Identification: A Coupled Forward-Inverse Approach

The Woods-Saxon mean-field approach offers a compact description of bound single-particle motion in finite nuclei, while physics-informed neural networks (PINNs) provide a differentiable route to the inverse problem of recovering potential parameters from sparse spectral data. We develop a probabilistic physics-informed framework in which a WaveNet represents the separated single-particle wavefunction and a ParamNet maps selected spectra, nuclear descriptors, and quantum numbers to a learned distribution over six global Woods-Saxon parameters. The Hamiltonian includes the central Woods-Saxon, proton Coulomb, and spin-orbit terms; training enforces spectral energy consistency, Schr"odinger-equation residuals, boundary conditions, normalization, orthogonality, spin-orbit splitting constraints, and latent regularization. The distribution mean serves as a selection-free parameter estimate, validated against an independent finite-difference radial solver. Synthetic closure tests with the Seminole and Wahlborn parameterizations recover all six parameters with sub-percent relative errors and reproduce reference spectra with mean absolute deviations of (0.0109) and (0.0131~\mathrm{MeV}). For experimental spectra with the Wahlborn form, the estimator reduces the all-state mean absolute error from (1.0783) to (0.8303~\mathrm{MeV}); with the Seminole form it attains (0.8068~\mathrm{MeV}), close to the (0.7969~\mathrm{MeV}) from Seminole reference parameters, using only (42) experimental levels -- about (51%) fewer than the Seminole calibration. These results show that sparse, structured single-particle spectra can constrain global Woods-Saxon interactions within a differentiable framework addressing both the forward eigenvalue problem and inverse parameter identification, while the learned output spread offers a model-derived, qualitative measure of parameter stiffness.

cs.CE↗

arXiv2026

Towards trajectory-unsupervised physics-informed neural solvers for molecular dynamics

Molecular dynamics (MD) simulations are governed by explicit equations of motion, yet most neural approaches that accelerate or emulate MD rely on simulator-generated trajectories, forces, or energies for training. In this work we ask to what extent can physically meaningful molecular trajectories be recovered from the governing laws. We introduce the Differentiable Newtonian Molecular Solver (DINaMo), a physics-informed neural framework that represents molecular trajectories as differentiable functions of time and is trained exclusively through Newtonian dynamics, conservation laws, and analytic interaction potentials on a given equilibrated initial state. Unlike prior physics-informed MD formulations, DINaMo uses no simulator-generated trajectories, forces, velocities, or energies as supervisory targets. In Lennard--Jones argon systems, the learned trajectories reproduce short-time coordinate, energy, and structural observables, including in a larger and denser liquid-like setting where the radial distribution function is recovered. Although currently limited to short temporal horizons, the results indicate that physically meaningful molecular trajectories can emerge directly from physics-only supervision, supporting the feasibility of trajectory-unsupervised neural solvers for molecular dynamics.

cs.CE↗

arXiv2026

An Explainable Physics-Informed Neural Frequency-Response Framework for Shunt-Parameter Identification in Semi-Active Piezoelectric Tuned Mass Dampers

This paper proposes a Physics-Informed Neural Frequency Response Framework for learning and interpreting the frequency-domain behavior of semi-active shunted piezoelectric tuned mass dampers. The motivation is that the behavior of such systems is most naturally expressed through frequency response functions, while the governing electromechanical interactions depend strongly on hidden structural and shunt parameters. Conventional data-driven models can approximate these mappings, but they often lack physical consistency, require large training datasets, and provide limited interpretability. To address these limitations, the proposed framework combines a physics-based forward frequency-response model, a neural inverse learning module, and an explainability component. The forward model is used to generate synthetic complex-valued frequency-response data over a broad range of structural and shunt configurations while preserving the governing electromechanical behavior of the system. Based on synthetic frequency-response data, the neural inverse model is trained to estimate hidden parameters from spectral response signatures and is subsequently evaluated using independently measured experimental FRFs. This synthetic-to-experimental design enables fast parameter inference without solving a new optimization problem for each measured case. To improve robustness to realistic conditions, controlled noise is introduced only at the inverse-training stage, while the underlying physics model remains noise-free. In addition, the learned representation is analyzed through latent-space organization, sensitivity mapping, and reduced symbolic distillation in order to extract interpretable electromechanical response descriptors. The resulting framework provides a data-efficient and explainable ML approach for frequency-response-based identification and inverse tuning of STMD.

cs.CE↗

arXiv2026

Active Learning Guided Design Space Refinement for Scalable Multi-Objective Bayesian Optimization in Materials Discovery

Advanced materials discovery increasingly relies on machine learning and Bayesian optimization to explore large discrete design spaces under limited evaluation budgets. However, conventional Bayesian optimization (BO) can become inefficient as candidate spaces grow, often evaluating low-value regions before reaching informative areas. We propose an active-learning (AL)-guided adaptive search-space refinement framework combined with multi-objective BO to accelerate materials optimization while preserving Pareto-relevant regions. We evaluate the approach on CH4/N2 separation in covalent-organic frameworks and pressure-vessel design with material-direction stress components and thickness objectives. Results show that the AL-guided refinement reduces the candidate space by approximately half while preserving more than 99 percent of the original hypervolume. The reduced-space strategy improves early convergence and cumulative Pareto-front discovery from the BO, demonstrating efficient large-scale materials optimization across constrained autonomous materials discovery settings.

cs.LG↗

arXiv2026

Frugal Bayesian Optimization: Scalable Surrogates for Data- and Resource-Limited Discovery

Bayesian Optimization (BO) is widely adopted for data-efficient optimization in scientific and engineering applications, yet its computational cost is rarely evaluated alongside optimization performance. Here we present a systematic, compute-aware study of BO that evaluates surrogate models along two axes: optimization quality and computational frugality. Across eight benchmark functions and nine real-world datasets spanning materials science, mechanics, robotics, chemistry, and machine learning, we benchmark four surrogate models: Gaussian Processes, Random Forests, NGBoost, and Bayesian Adaptive Spline Surfaces. We show that Gaussian Process-based BO consistently incurs the highest time and memory overhead without delivering superior optimization or sample efficiency. In contrast, scalable alternatives achieve equal or better performance at a fraction of the computational cost. Motivated by these findings, we introduce a surrogate-recommendation framework that predicts the most suitable BO surrogate from inexpensive dataset characteristics. Together, these results establish FruBO as a reproducible, compute-aware baseline for Bayesian Optimization and provide practical guidance for surrogate selection under limited computational and experimental budgets.

cs.LG↗