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Machine Learning Power Week 2023: Clustering in Hadronic Calorimeters
Muaz Al Halabi (ed), Marcel Bajdel (ed), Jeroen Peter Bormans (ed), Hannah Bossi (ed), Maria Calmon Behling (ed), Florian Ehmann (ed), Niklas Gotz (ed), Jerome Jung (ed), Rafet Kavak (ed), Mario Kr\"uger (ed), Robin Lakos (ed), Annemarie Lauterbach (ed), Patrick Mccormack (ed), Akhil Mithran (ed), Daniel Murnane (ed), Mathis Nolte (ed), Tim Rogoschinski (ed), Jan Scharf (ed), Oddharak Tyagi (ed), Liv V{\aa}ge (e…

Machine Learning Power Week 2023: Clustering in Hadronic Calorimeters
In both high-energy physics and industry applications, a crowd-sourced approach to difficult problems is becoming increasingly common. These innovative approaches are ideal for the development of future facilities where the simulations can be publicly distributed, such as the Electron-Ion Collider (EIC). In this paper, we discuss a so-called ``Power Week" where graduate students were able to learn about machine learning while also contributing to an unsolved problem at a future facility. Here, …

Comprehensive Mapping of Tracer Diffusivities Across Composition Space in Ternary NiAlTi and Quinary NiCoFeAlTi High-Entropy Alloy Using Diffusion Couple Experiments and Physics Informed Neural Network Inversion
Ismail Kamil Worke, Suman Sadhu, Saswata Bhattacharyya, Aloke Paul
https://arxiv.org/abs/2509.09486

Comprehensive Mapping of Tracer Diffusivities Across Composition Space in Ternary NiAlTi and Quinary NiCoFeAlTi High-Entropy Alloy Using Diffusion Couple Experiments and Physics Informed Neural Network Inversion
A comprehensive experimental and physics informed neural network numerical inverse diffusion analysis is conducted in technologically important NiAlTi ternary and NiCoFeAlTi quinary solid solutions for estimating and extracting composition dependent diffusion coefficients. A systematic variation of tracer, intrinsic and interdiffusion coefficients with composition could be estimated in the ternary solid solution. Following, the possibility of producing Al Ti constant PB diffusion profiles keepi…

SEDONA-GesaRaT: an AI-Accelerated Radiative Transfer Program for 3-D Supernova Simulations
Xingzhuo Chen, Ulisses Braga-Neto, Lifan Wang, Daniel Kasen, Zhengwei Liu, F. K. Roepke, Ming Zhong, David J. Jeffery
https://arxiv.org/abs/2507.11767

SEDONA-GesaRaT: an AI-Accelerated Radiative Transfer Program for 3-D Supernova Simulations
We present SEDONA-GesaRaT, a rapid code for supernova radiative transfer simulation developed based on the Monte-Carlo radiative transfer code SEDONA. We use a set of atomic physics neural networks (APNN), an artificial intelligence (AI) solver for the non-local thermodynamic equilibrium (NLTE) atomic physics level population calculation, which is trained and validated on 119 1-D type Ia supernova (SN Ia) radiative transfer simulation results showing great computation speed and accuracy. SEDONA…