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From the 1 of 8 linked papers with an AI index.

activity
20242026
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8 papers

physics.flu-dyn2026

A sub-grid-scale model for polydisperse bubbly flows with heat and mass transfer

Anand Radhakrishnan, Spencer H. Bryngelson

The paper introduces a sub‑grid‑scale model that uses a conditional hyperbolic quadrature method to track bubble pressure and vapor mass in polydisperse bubbly flows, explicitly ac…

cs.AR2026

Apple Neural Engine: Architecture, Programming, and Performance

Spencer H. Bryngelson

The Apple Neural Engine (ANE) is the fixed-function matrix accelerator that has shipped in Apple systems-on-chip since the A11-class iPhone and iPad chips and the M1-class Mac chip…

cs.PL2026

ANEForge: Python for direct computation on the Apple Neural Engine

Spencer H. Bryngelson

ANEForge is a Python package that programs the Apple Neural Engine (ANE), the fixed-function neural accelerator on every recent Apple device, directly and without CoreML. In produc…

physics.flu-dyn2025

MFC 5.0: An exascale many-physics flow solver

Benjamin Wilfong, Henry A. Le Berre, Anand Radhakrishnan +16

Many problems of interest in engineering, medicine, and the fundamental sciences rely on high-fidelity flow simulation, making performant computational fluid dynamics solvers a mai…

physics.flu-dyn2025

Hierarchical Bayesian constitutive model selection for high-strain-rate soft material characterization

Victor Sanchez, Sawyer Remillard, Bachir A. Abeid +5

The high-fidelity characterization of soft, tissue-like materials under ultra-high-strain-rate conditions is critical in engineering and medicine. Still, it remains challenging due…

physics.comp-ph2025

Accelerating Bayesian Optimal Experimental Design via Local Radial Basis Functions: Application to Soft Material Characterization

Tianyi Chu, Jonathan B. Estrada, Spencer H. Bryngelson

We develop a computational approach that significantly improves the efficiency of Bayesian optimal experimental design (BOED) using local radial basis functions (RBFs). The present…