arXiv:2606. 04033v2 Announce Type: replace Abstract: The validation of advanced nuclear reactor designs and fuel concepts will require the design of new critical experiments with high neutronic similarity to the target technology.
arXiv:2606. 04033v1 Announce Type: new Abstract: The validation of advanced nuclear reactor designs and fuel concepts requires critical experiments with high neutronic similarity to the target technology.
arXiv:2607. 04450v1 Announce Type: cross Abstract: Integral codes like the Accident Source Term Evaluation Code (ASTEC) are powerful tools to study the physics of Severe Accidents (SAs) in nuclear reactors.
The paper introduces JI-PINN, a joint initialization strategy for physics-informed neural networks that simultaneously initializes neutron flux and the effective multiplication factor (keff) using a low-resolution approximate solution. By jointly optimizing both quantities under physical constraints, the method achieves significant reductions in computational time—up to nearly 50%—across various benchmark neutron diffusion problems while maintaining accuracy. It also reduces anomalous keff deviations, offering a more robust approach to solving K‑eigenvalue problems with PINNs.
Efficient determination of the effective multiplication factor (keff) is an important computational task in reactor core neutronics analysis. Physics-informed neural networks (PINNs) incorporate neutr...
The paper explores global sensitivity analysis (GSA) when both physics-based models and experimental data are available, focusing on physics-informed machine learning to improve sensitivity estimates. It evaluates two ML approaches—deep neural networks (DNN) and Gaussian processes (GP)—and two physics integration strategies: physics-constrained loss functions and sequential pre‑training with simulation followed by experimental fine‑tuning. Four models per ML type are constructed, incorporating model uncertainties into Sobol index calculations, and results show DNNs yield tighter sensitivity bounds than GP models, demonstrated on additive manufacturing and lake temperature examples.
arXiv:2512. 24116v3 Announce Type: replace-cross Abstract: Parton Distribution Functions (PDFs) play a central role in describing experimental data at colliders and provide insight into the structure of nucleons.
arXiv:2406.04261v2 Announce Type: replace Abstract: Simulators are vital in science and engineering, as they faithfully model the influence of design parameters on real-world observations. Optimizing...
The paper introduces Structured Representation Learning for Behavior Cloning (SRLBC), an architecture that separates embeddings for slow, fast, and exogenous state variables in multiscale dynamical systems. Applied to a pressurized water reactor load‑following benchmark, SRLBC outperforms shared‑embedding baselines in cumulative cost, imitation accuracy, and constraint satisfaction, achieving near‑NMPC performance at 15× lower computational cost. When combined with a safety‑augmented approximate NMPC, the resulting controller is 6.8× faster than full NMPC while maintaining at least 99.8% feasibility, and it degrades least under distribution shift.
The paper introduces pcNN‑SN, a physics‑enhanced predictor‑corrector neural network algorithm that couples with the discrete ordinates (SN) method for neutron transport equations. By using high‑ and low‑accuracy data, the network predicts correction factors for coarse‑grid solutions, incorporating local physical features and dimensionless transformations to reduce input dimensionality. The algorithm maintains conservation properties and, when coupled with SN sweeps, achieves fine‑grid accuracy while reducing computation time by over 60% on a three‑dimensional test problem.
This paper introduces a polyconvex physics‑augmented neural network (PANN) that uses learned paired directional features to model the anisotropic hyperelastic response of a representative volume element. The network’s energy functions are constructed with convex, componentwise nondecreasing cores (ICNN or ICKAN) and satisfy objectivity, stress‑free reference, two‑dimensional polyconvexity, and prescribed growth conditions. Experiments on multicavity and single‑cavity periodic cells show that the constrained PANN achieves sub‑percent stress and displacement errors, while an unconstrained model loses rank‑one convexity and under‑predicts compression stresses beyond the training data.
arXiv:2608. 04027v1 Announce Type: new Abstract: This work investigates the feasibility of augmenting traditional R-Matrix codes with a robust machine learning framework for automatically detecting neutron resonances in transmission spectra.
arXiv:2606. 20442v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) solve Partial Differential Equations (PDEs) by embedding physical laws into neural network training.