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Nitin Daphalapurkar

Publications and source records attributed to Nitin Daphalapurkar.

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A Comparison between Separately Calibrated P-α and Mesoscale Models for Weak Shock Compaction of Granular Sugar

This study compares calibration strategies for predicting particle velocity in granular sugar subjected to weak shock loading, using measurements from flyer-plate impact experiments as a benchmark. Two computational approaches are evaluated: a continuum-based P-alpha Menikoff model requiring calibration of effective constitutive parameters, and mesoscale simulations that explicitly resolve grain geometry and porosity. Both models can match the measured particle-velocity histories, but only through fundamentally different calibration mechanisms. In the P-alpha model, a pressure-dependent yield strength is essential and the response remains highly sensitive to parameter choices such as the crush-out pressure. In contrast, mesoscale simulations are far less sensitive to parameter tuning and instead depend primarily on the physical state variable of porosity, represented in 2D through an equivalent mapping of the 3D specimen. These results show that continuum parameters act as effective surrogates for underlying grain-scale processes, whereas mesoscale modeling identifies porosity as the dominant control on macroscopic wave onset, highlighting distinct calibration pathways and interpretive implications for each modeling approach.

physics.comp-ph

RSR-NF: Neural Field Regularization by Static Restoration Priors for Dynamic Imaging

Dynamic imaging involves the reconstruction of a spatio-temporal object at all times using its undersampled measurements. In particular, in dynamic computed tomography (dCT), only a single projection at one view angle is available at a time, making the inverse problem very challenging. Moreover, ground-truth dynamic data is usually either unavailable or too scarce to be used for supervised learning techniques. To tackle this problem, we propose RSR-NF, which uses a neural field (NF) to represent the dynamic object and, using the Regularization-by-Denoising (RED) framework, incorporates an additional static deep spatial prior into a variational formulation via a learned restoration operator. We use an ADMM-based algorithm with variable splitting to efficiently optimize the variational objective. We compare RSR-NF to three alternatives: NF with only temporal regularization; a recent method combining a partially-separable low-rank representation with RED using a denoiser pretrained on static data; and a deep-image prior-based model. The first comparison demonstrates the reconstruction improvements achieved by combining the NF representation with static restoration priors, whereas the other two demonstrate the improvement over state-of-the art techniques for dCT.

eess.IV