arXiv:2512. 23234v3 Announce Type: replace-cross Abstract: Infrared gas leak detection is important for industrial safety and environmental monitoring, but automatic detection remains challenging because gas plumes are often faint, small, semi-transparent, and weakly bounded.
By Dongsheng Li, Tianli Ma, Siling Wang, Beibei Duan, Song Gao
arXiv:2609.22329v1 Announce Type: cross
Abstract: Black-Box Optimization (BBO) is often applied in several engineering fields and can utilize an advancement of numerical measure- ments and simulation...
By Md Khadimul Islam Zim (Czech Academy of Sciences, Institute of Computer Science, Prague, Czech Republic), Martin Hole\v{n}a (Czech Academy of Sciences, Institute of Computer Science, Prague, Czech Republic)
The paper introduces the simultaneous neural topology optimization (SNTO) framework, which uses custom neural networks to jointly represent state, adjoint, and design variables in a single optimization loop. SNTO incorporates a multi-component loss function that balances design objectives, aggregated stress constraints, and potential energy, and employs a hardware‑aware training scheme that partitions the design domain across multiple GPUs to handle millions of degrees of freedom. Validation on 2D benchmarks shows super‑resolution designs with sharper boundaries and lower volume fractions, while large‑scale 3D tests demonstrate scalability to eight million collocation points on eight GPUs.
By Xiangyu Sun, Shirin Hosseinmardi, Ramin Bostanabad
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.
By Will Savage, Logan Burnett, Dean Price
ChannelFlow-Tools is an open‑source, configuration‑driven pipeline that generates machine‑learning‑ready datasets for three‑dimensional obstructed channel flows. It combines procedural obstacle geometry generation across six shape families, signed‑distance‑field voxelisation, lattice‑Boltzmann simulation, and packaging into ML‑ready tensors, all driven by reproducible configuration files. The pipeline is validated through mesh‑integrity audits, SDF representation checks, solver benchmarks, and data‑integrity audits, and it has been used to train surrogate models (3D U‑Net, FNO, U‑FNO) that learn geometry‑to‑flow mappings and exhibit physically interpretable behaviour on out‑of‑distribution splits.
By Shubham Kavane, Lukas Schr\"oder, Kajol Kulkarni, Fernando Gonzalez, Harald Koestler
arXiv:2608. 03589v1 Announce Type: new Abstract: We present a method for designing deep neural networks (DNNs) for intermittent, energy-autonomous, on-device learning on microcontroller units (MCUs).
By Jakob Schubert, Maximilian Kasper, Maximilian Linke, Benedict Herzog, Mark Deutel, Axel Plinge, Dominik Seuss, Christopher Mutschler