Search Results for author: Rishikesh Ranade

Found 15 papers, 1 papers with code

Diffusion model based data generation for partial differential equations

no code implementations19 Jun 2023 Rucha Apte, Sheel Nidhan, Rishikesh Ranade, Jay Pathak

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.

NLP Inspired Training Mechanics For Modeling Transient Dynamics

no code implementations4 Nov 2022 Lalit Ghule, Rishikesh Ranade, Jay Pathak

In recent years, Machine learning (ML) techniques developed for Natural Language Processing (NLP) have permeated into developing better computer vision algorithms.

A composable machine-learning approach for steady-state simulations on high-resolution grids

no code implementations11 Oct 2022 Rishikesh Ranade, Chris Hill, Lalit Ghule, Jay Pathak

The numerical experiments show that our approach outperforms ML baselines in terms of 1) accuracy across quantitative metrics and 2) generalization to out-of-distribution conditions as well as domain sizes.

A Thermal Machine Learning Solver For Chip Simulation

no code implementations10 Sep 2022 Rishikesh Ranade, Haiyang He, Jay Pathak, Norman Chang, Akhilesh Kumar, Jimin Wen

Thermal analysis provides deeper insights into electronic chips behavior under different temperature scenarios and enables faster design exploration.

A Hybrid Iterative Numerical Transferable Solver (HINTS) for PDEs Based on Deep Operator Network and Relaxation Methods

no code implementations28 Aug 2022 Enrui Zhang, Adar Kahana, Eli Turkel, Rishikesh Ranade, Jay Pathak, George Em Karniadakis

Based on recent advances in scientific deep learning for operator regression, we propose HINTS, a hybrid, iterative, numerical, and transferable solver for differential equations.

A composable autoencoder-based iterative algorithm for accelerating numerical simulations

no code implementations7 Oct 2021 Rishikesh Ranade, Chris Hill, Haiyang He, Amir Maleki, Norman Chang, Jay Pathak

Numerical simulations for engineering applications solve partial differential equations (PDE) to model various physical processes.

BIG-bench Machine Learning

A composable autoencoder-based algorithm for accelerating numerical simulations

no code implementations29 Sep 2021 Rishikesh Ranade, Derek Christopher Hill, Haiyang He, Amir Maleki, Norman Chang, Jay Pathak

Numerical simulations for engineering applications solve partial differential equations (PDE) to model various physical processes.

One-shot learning for solution operators of partial differential equations

no code implementations6 Apr 2021 Anran Jiao, Haiyang He, Rishikesh Ranade, Jay Pathak, Lu Lu

Discovering 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.

One-Shot Learning Operator learning

A Latent space solver for PDE generalization

no code implementations6 Apr 2021 Rishikesh Ranade, Chris Hill, Haiyang He, Amir Maleki, Jay Pathak

In this work we propose a hybrid solver to solve partial differential equation (PDE)s in the latent space.

Generalized Joint Probability Density Function Formulation inTurbulent Combustion using DeepONet

no code implementations5 Apr 2021 Rishikesh Ranade, Kevin Gitushi, Tarek Echekki

The DeepONet is a machine learning model that is parameterized on the unconditional means of PCs at a given spatial location and discrete PC coordinates and predicts the joint probability density value for the corresponding PC coordinate.

Density Estimation

ActivationNet: Representation learning to predict contact quality of interacting 3-D surfaces in engineering designs

no code implementations21 Mar 2021 Rishikesh Ranade, Jay Pathak

In machine learning applications, 3-D surfaces are most suitably represented with point clouds or meshes and learning representations of interacting geometries form point-based representations is challenging.

BIG-bench Machine Learning Representation Learning

Algorithmically-Consistent Deep Learning Frameworks for Structural Topology Optimization

no code implementations9 Dec 2020 Jaydeep Rade, Aditya Balu, Ethan Herron, Jay Pathak, Rishikesh Ranade, Soumik Sarkar, Adarsh Krishnamurthy

We achieve this by training multiple networks, each learning a different step of the overall topology optimization methodology, making the framework more consistent with the topology optimization algorithm.

BIG-bench Machine Learning

An Efficient Machine-Learning Approach for PDF Tabulation in Turbulent Combustion Closure

no code implementations18 May 2020 Rishikesh Ranade, Genong Li, Shaoping Li, Tarek Echekki

In this work, we address these issues by introducing an adaptive training algorithm that relies on multi-layer perception (MLP) neural networks for regression and self-organizing maps (SOMs) for clustering data to tabulate using different networks.

BIG-bench Machine Learning Clustering

DiscretizationNet: A Machine-Learning based solver for Navier-Stokes Equations using Finite Volume Discretization

no code implementations17 May 2020 Rishikesh Ranade, Chris Hill, Jay Pathak

The two solver characteristics that have been adopted in this work are: 1) the use of discretization-based schemes to approximate spatio-temporal partial derivatives and 2) the use of iterative algorithms to solve linearized PDEs in their discrete form.

BIG-bench Machine Learning

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