Rishikesh Ranade
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A Benchmarking Framework for AI models in Automotive Aerodynamics Open
In this paper, we introduce a benchmarking framework within the open-source NVIDIA PhysicsNeMo-CFD framework designed to systematically assess the accuracy, performance, scalability, and generalization capabilities of AI models for automot…
Accelerating Transient CFD through Machine Learning-Based Flow Initialization Open
Transient computational fluid dynamics (CFD) simulations are essential for many industrial applications, but suffer from high compute costs relative to steady-state simulations. This is due to the need to: (a) reach statistical steadiness …
DoMINO: A Decomposable Multi-scale Iterative Neural Operator for Modeling Large Scale Engineering Simulations Open
Numerical simulations play a critical role in design and development of engineering products and processes. Traditional computational methods, such as CFD, can provide accurate predictions but are computationally expensive, particularly fo…
A domain decomposition-based autoregressive deep learning model for unsteady and nonlinear partial differential equations Open
In this paper, we propose a domain-decomposition-based deep learning (DL) framework, named transient-CoMLSim, for accurately modeling unsteady and nonlinear partial differential equations (PDEs). The framework consists of two key component…
Fast meta-solvers for 3D complex-shape scatterers using neural operators trained on a non-scattering problem Open
Three-dimensional target identification using scattering techniques requires high accuracy solutions and very fast computations for real-time predictions in some critical applications. We first train a deep neural operator~(DeepONet) to so…
Sampling-based Distributed Training with Message Passing Neural Network Open
In this study, we introduce a domain-decomposition-based distributed training and inference approach for message-passing neural networks (MPNN). Our objective is to address the challenge of scaling edge-based graph neural networks as the n…
Diffusion model based data generation for partial differential equations Open
In a preliminary attempt to address the problem of data scarcity in physics-based machine learning, we introduce a novel methodology for data generation in physics-based simulations. Our motivation is to overcome the limitations posed by t…
NLP Inspired Training Mechanics For Modeling Transient Dynamics Open
In recent years, Machine learning (ML) techniques developed for Natural Language Processing (NLP) have permeated into developing better computer vision algorithms. In this work, we use such NLP-inspired techniques to improve the accuracy, …
On the Geometry Transferability of the Hybrid Iterative Numerical Solver for Differential Equations Open
The discovery of fast numerical solvers prompted a clear and rapid shift towards iterative techniques in many applications, especially in computational mechanics, due to the increased necessity for solving very large linear systems. Most n…
View article: A composable machine-learning approach for steady-state simulations on high-resolution grids
A composable machine-learning approach for steady-state simulations on high-resolution grids Open
In this paper we show that our Machine Learning (ML) approach, CoMLSim (Composable Machine Learning Simulator), can simulate PDEs on highly-resolved grids with higher accuracy and generalization to out-of-distribution source terms and geom…
A Thermal Machine Learning Solver For Chip Simulation Open
Thermal analysis provides deeper insights into electronic chips behavior under different temperature scenarios and enables faster design exploration. However, obtaining detailed and accurate thermal profile on chip is very time-consuming u…
Blending Neural Operators and Relaxation Methods in PDE Numerical Solvers Open
Neural networks suffer from spectral bias having difficulty in representing the high frequency components of a function while relaxation methods can resolve high frequencies efficiently but stall at moderate to low frequencies. We exploit …
A composable autoencoder-based iterative algorithm for accelerating numerical simulations Open
Numerical simulations for engineering applications solve partial differential equations (PDE) to model various physical processes. Traditional PDE solvers are very accurate but computationally costly. On the other hand, Machine Learning (M…
Geometry encoding for numerical simulations Open
We present a notion of geometry encoding suitable for machine learning-based numerical simulation. In particular, we delineate how this notion of encoding is different than other encoding algorithms commonly used in other disciplines such …
A Latent space solver for PDE generalization Open
In this work we propose a hybrid solver to solve partial differential equation (PDE)s in the latent space. The solver uses an iterative inferencing strategy combined with solution initialization to improve generalization of PDE solutions. …
One-shot learning for solution operators of partial differential equations Open
Learning and solving governing equations of a physical system, represented by partial differential equations (PDEs), from data is a central challenge in a variety of areas of science and engineering. Traditional numerical methods for solvi…
Generalized Joint Probability Density Function Formulation inTurbulent Combustion using DeepONet Open
Joint probability density function (PDF)-based models in turbulent combustion provide direct closure for turbulence-chemistry interactions. The joint PDFs capture the turbulent flame dynamics at different spatial locations and hence it is …
ActivationNet: Representation learning to predict contact quality of interacting 3-D surfaces in engineering designs Open
Engineering simulations for analysis of structural and fluid systems require information of contacts between various 3-D surfaces of the geometry to accurately model the physics between them. In machine learning applications, 3-D surfaces …
Physics-consistent deep learning for structural topology optimization. Open
Topology optimization has emerged as a popular approach to refine a component's design and increasing its performance. However, current state-of-the-art topology optimization frameworks are compute-intensive, mainly due to multiple finite …
A Framework for Data-Based Turbulent Combustion Closure: A Priori Validation Open
Experimental multi-scalar measurements in laboratory flames have provided important databases for the validation of turbulent combustion closure models. In this work, we present a framework for data-based closure in turbulent combustion an…