arXiv Machine Learning

Inverse Critical Experiment Design via Gradient Optimization and a Multigroup Attention-Based Neural Network Architecture

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 AI

Jul 7

A Deep Learning-based surrogate model for Severe Accidents in nuclear reactors using ASTEC

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.

By Alessandro Longhi, Danny Lathouwers, Zolt\'an Perk\'o

arXiv AI

Aug 6

NuclearDiffusion: Text-to-Image Foundation Models for Learning Nuclear Energy Concepts

arXiv:2608. 04030v1 Announce Type: cross Abstract: Generative artificial intelligence (AI) has transformed text-to-image synthesis, yet its ability to represent specialized engineering domains remains largely unexplored.

By Mohammed I. Radaideh, Jeremy Moon, Andre Gala-Garza, Emma Son, Yug Shah, Majdi I. Radaideh

llms diffusion fine-tuning benchmarks

arXiv Machine Learning

Jun 30

Implementation of Hyperelastic Physics-Augmented Neural Networks in the Explicit Finite Element Codes Simcenter Radioss and OpenRadioss with Applications to Impact Events

arXiv:2606. 29874v1 Announce Type: cross Abstract: Data-driven material modeling techniques have gained significant attention due to their ability to capture complex constitutive behaviors beyond the limitations of classical material models.

By Lukas Maurer, Sascha Eisentr\"ager, Marian Bulla, Daniel Juhre

Hugging Face Trending Papers

6d ago

Fine-tuned Normalizing Flows for ALICE Zero Degree Calorimeter Fast Simulation

Simulating the ALICE Zero Degree Calorimeter (ZDC) neutron detector responses at the LHC is computationally expensive, requiring complex Monte Carlo chains. We develop a generative surrogate, focusing on Normalizing Flows (NFs).

fine-tuning

arXiv Machine Learning

Jul 28

WeCon: An Efficient Weight-Conditioned Neural Solver for Multi-Objective Combinatorial Optimization Problems

arXiv:2605. 22876v2 Announce Type: replace Abstract: Existing neural solvers for Multi-Objective Combinatorial Optimization Problems (MOCOPs) commonly adopt decomposition-based strategies that scalarize a MOCOP into multiple subproblems associated with distinct weight vectors.

By Xuan Wu, Jinbiao Chen, Yang Li, Lijie Wen, Chunguo Wu, Yuanshu Li, Yubin Xiao, Chunyan Miao, You Zhou, Di Wang

benchmarks

arXiv Machine Learning

5d ago

Fine-tuned Normalizing Flows for ALICE Zero Degree Calorimeter Fast Simulation

arXiv:2608. 12795v1 Announce Type: cross Abstract: Simulating the ALICE Zero Degree Calorimeter (ZDC) neutron detector responses at the LHC is computationally expensive, requiring complex Monte Carlo chains.

By Emilia Majerz, Jacek Otwinowski, Witold Dzwinel, Jacek Kitowski

fine-tuning

arXiv Machine Learning

Jul 16

Microstructure-Conditioned Surrogate Models for Graded Multiscale Optimization of Mycelium Composites

arXiv:2607. 13688v1 Announce Type: new Abstract: Emerging sustainable materials increasingly rely on engineered hierarchy and microstructure to achieve control of their properties and mechanical behavior.

By J. Storm, I. B. C. M. Rocha, S. Schyck, K. Masania, F. P. van der Meer

arXiv Machine Learning

Jul 23

NeuroPareto: Calibrated Acquisition for Costly Many-Goal Search in Vast Parameter Spaces

arXiv:2602. 03901v5 Announce Type: replace Abstract: The pursuit of optimal trade-offs in high-dimensional search spaces under stringent computational constraints poses a fundamental challenge for contemporary multi-objective optimization.

By Rong Fu, Chunlei Meng, Haoyu Zhao, Kun Liu, JiaBao Dou, Youjin Wang, Simon James Fong

arXiv Machine Learning

Jun 19

Evolutionary Two-Stage Hyperparameter Optimization Strategies for Physics-Informed Neural Networks

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.

By Fedor Buzaev (HSE University), Dmitry Efremenko (HSE University), Egor Bugaev (HSE University), Andrei Ermakov (HSE University, AXXX), Denis Derkach (HSE University), Daria Pugacheva (HSE University, AXXX), Fedor Ratnikov (HSE University)

rag

arXiv Machine Learning

Jun 4

U-Net-Accelerated Quality-Diversity Optimization for Climate-Adaptive Urban Layouts

arXiv:2606. 04658v1 Announce Type: cross Abstract: Optimizing urban layouts for climate adaptation requires balancing building density with cold-air ventilation.

By Alexander Hagg, Tania Guerrero, Dirk Reith

safety

arXiv Machine Learning

Jun 16

Machine Learning-Driven Chemical Reactor Network Modeling of the Sandia-D Flame

arXiv:2606. 14729v1 Announce Type: cross Abstract: Turbulent combustion simulations are crucial for many scientific and engineering systems.

By Nicolas J. Tricard, Benjamin C. Koenig, Sili Deng

diffusion

arXiv AI

2d ago

Designing Compact Neural Architectures via Neuron Gating and Mixed Activation

arXiv:2608. 14443v1 Announce Type: cross Abstract: Neural Architecture Search (NAS) is naturally formulated as a bilevel optimization problem, where the upper-level optimizes the architecture using validation performance and the lower-level trains network parameters using training loss.

By Abhishek Shukla, Ankur Sinha, Faiz Hamid

llms

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