2 citations · 2 across the 3 of their papers we have counts for
5 papers · 1 filter
Physics-informed AI Accelerated Retention Analysis of Ferroelectric Vertical NAND: From Day-Scale TCAD to Second-Scale Surrogate Model
Gyujun Jeong, Sungwon Cho, Minji Shon +13
Ferroelectric field-effect transistors (FeFET)-based vertical NAND (Fe-VNAND) has emerged as a promising candidate to overcome z-scaling limitations with lower programming voltages…
GeoTransolver: Learning Physics on Irregular Domains Using Multi-scale Geometry Aware Physics Attention Transformer
Corey Adams, Rishikesh Ranade, Ram Cherukuri +1
We present GeoTransolver, a multiscale geometry-aware physics attention transformer for Computer Aided Engineering (CAE). GeoTransolver extends the Transolver backbone with GALE (G…
Automotive Crash Dynamics Modeling Accelerated with Machine Learning
Mohammad Amin Nabian, Sudeep Chavare, Deepak Akhare +3
Crashworthiness assessment is a critical aspect of automotive design, traditionally relying on high-fidelity finite element (FE) simulations that are computationally expensive and…
A Benchmarking Framework for AI models in Automotive Aerodynamics
Kaustubh Tangsali, Rishikesh Ranade, Mohammad Amin Nabian +5
In this paper, we introduce a benchmarking framework within the open-source NVIDIA PhysicsNeMo-CFD framework designed to systematically assess the accuracy, performance, scalabilit…
DoMINO: A Decomposable Multi-scale Iterative Neural Operator for Modeling Large Scale Engineering Simulations
Rishikesh Ranade, Mohammad Amin Nabian, Kaustubh Tangsali +4
Numerical simulations play a critical role in design and development of engineering products and processes. Traditional computational methods, such as CFD, can provide accurate pre…