BRICKS: Compositional Neural Markov Kernels for Zero-Shot Radiation-Matter Simulation

• URL: http://arxiv.org/abs/2605.06591v1
• Date: Thu, 07 May 2026 17:19:31 GMT
• Title: BRICKS: Compositional Neural Markov Kernels for Zero-Shot Radiation-Matter Simulation
• Abstract summary: We introduce a new strategy for compositional neural surrogates for radiation-matter interactions.<n>Exploiting the locality and the Markov nature of particle interactions, we create a emphnext-particle prediction kernel.<n>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.
• Score: 2.2650024890957385
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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