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Shobhan Roy

Publications and source records attributed to Shobhan Roy.

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High-fidelity simulations of shock initiation of an energetic crystal-binder system due to flyer impact

Meso-scale simulations of energy localization at hotspots provide closure models for multiscale frameworks of shock-to-detonation transition (SDT). Validation of such meso-scale calculations is challenging as direct comparison with experiments is constrained both by limitations of data acquisition in the experiments (e.g., of temperature fields) and modeling over-simplifications in the simulations. To address the latter problem and bring modeling closer to experiments, we advance a high-fidelity meso-scale computational framework for interface-resolved reactive calculations of shock initiation in plastic-bonded explosives (PBXs). Accurate resolution of shock and interfacial dynamics is achieved through higher-order (5th-order WENO) schemes, and sharp interface treatments are implemented for physically accurate material-material interactions. Recently obtained atomistics-consistent material models are used for HMX, with the grid resolution taken down to atomistic scale (O(nm)). The crystal geometries are obtained directly from experiments via nano-CT imaging. The impacting flyer plate, energetic crystal and binder are tracked as distinct phases, and flyer-binder impact and separation are simulated, capturing the flyer deformation and the effects of relief waves from the flyer surface. By combining these high-fidelity modeling components, we evaluate how closely simulations can approach experimental data. Overall, this work provides as assessment of which aspects of numerical treatment and material modeling have the greatest impact on meso-scale simulations of flyer-induced initiation of PBXs, and points to where further improvements are necessary.

physics.comp-ph

Size is Not the Solution: Deformable Convolutions for Effective Physics Aware Deep Learning

Physics-aware deep learning (PADL) enables rapid prediction of complex physical systems, yet current convolutional neural network (CNN) architectures struggle with highly nonlinear flows. While scaling model size addresses complexity in broader AI, this approach yields diminishing returns for physics modeling. Drawing inspiration from Hybrid Lagrangian-Eulerian (HLE) numerical methods, we introduce deformable physics-aware recurrent convolutions (D-PARC) to overcome the rigidity of CNNs. Across Burgers' equation, Navier-Stokes, and reactive flows, D-PARC achieves superior fidelity compared to substantially larger architectures. Analysis reveals that kernels display anti-clustering behavior, evolving into a learned "active filtration" strategy distinct from traditional h- or p-adaptivity. Effective receptive field analysis confirms that D-PARC autonomously concentrates resources in high-strain regions while coarsening focus elsewhere, mirroring adaptive refinement in computational mechanics. This demonstrates that physically intuitive architectural design can outperform parameter scaling, establishing that strategic learning in lean networks offers a more effective path forward for PADL than indiscriminate network expansion.

cs.LG