BRICKS: Compositional Neural Markov Kernels for Zero-Shot Radiation-Matter Simulation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911658317709312 |
|---|---|
| author | Hildebrandt, Richard Kourlitis, Evangelos Hashemi, Baran Bünstorf, Manuel Meyer, Thierry Boskov, Nikola Kagan, Michael Rosenbaum, Dan Ganguly, Sanmay Heinrich, Lukas |
| author_facet | Hildebrandt, Richard Kourlitis, Evangelos Hashemi, Baran Bünstorf, Manuel Meyer, Thierry Boskov, Nikola Kagan, Michael Rosenbaum, Dan Ganguly, Sanmay Heinrich, Lukas |
| contents | We introduce a new strategy for compositional neural surrogates for radiation-matter interactions, a key task spanning domains from particle physics through nuclear and space engineering to medical physics. Exploiting the locality and the Markov nature of particle interactions, we create a \emph{next-particle prediction} kernel using hybrid discrete-continuous transformer models based on Riemannian Flow Matching on product manifolds. The model generates variable-sized typed sets of particles and radiation side effects that are the result of the interaction of an incident particle with a material volume. The resulting kernel can be composed to simulate unseen large-scale material distributions in a zero-shot manner. Unlike mechanistic simulators, our model is designed to be differentiable, provides tractable likelihoods for future downstream applications. A significant computational speed-up on GPU compared to CPU-bound mechanistic simulation is observed for single-kernel execution. We evaluate the model at the kernel level and demonstrate predictive stability over multi-round autoregressive rollouts. We additionally release a novel 20M-event radiation-matter interaction dataset for further research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_06591 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | BRICKS: Compositional Neural Markov Kernels for Zero-Shot Radiation-Matter Simulation Hildebrandt, Richard Kourlitis, Evangelos Hashemi, Baran Bünstorf, Manuel Meyer, Thierry Boskov, Nikola Kagan, Michael Rosenbaum, Dan Ganguly, Sanmay Heinrich, Lukas Machine Learning High Energy Physics - Phenomenology We introduce a new strategy for compositional neural surrogates for radiation-matter interactions, a key task spanning domains from particle physics through nuclear and space engineering to medical physics. Exploiting the locality and the Markov nature of particle interactions, we create a \emph{next-particle prediction} kernel using hybrid discrete-continuous transformer models based on Riemannian Flow Matching on product manifolds. The model generates variable-sized typed sets of particles and radiation side effects that are the result of the interaction of an incident particle with a material volume. The resulting kernel can be composed to simulate unseen large-scale material distributions in a zero-shot manner. Unlike mechanistic simulators, our model is designed to be differentiable, provides tractable likelihoods for future downstream applications. A significant computational speed-up on GPU compared to CPU-bound mechanistic simulation is observed for single-kernel execution. We evaluate the model at the kernel level and demonstrate predictive stability over multi-round autoregressive rollouts. We additionally release a novel 20M-event radiation-matter interaction dataset for further research. |
| title | BRICKS: Compositional Neural Markov Kernels for Zero-Shot Radiation-Matter Simulation |
| topic | Machine Learning High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2605.06591 |