Search Results for author: Jaideep Pathak

Found 12 papers, 2 papers with code

DL-Corrector-Remapper: A grid-free bias-correction deep learning methodology for data-driven high-resolution global weather forecasting

no code implementations21 Oct 2022 Tao Ge, Jaideep Pathak, Akshay Subramaniam, Karthik Kashinath

The improvement in DLCR's performance against the gold standard ground truth over the baseline's performance shows its potential to correct, remap, and fine-tune the mesh-gridded forecasts under the supervision of observations.

Weather Forecasting

Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence

1 code implementation9 May 2022 Ashesh Chattopadhyay, Jaideep Pathak, Ebrahim Nabizadeh, Wahid Bhimji, Pedram Hassanzadeh

In this paper, we propose a convolutional variational autoencoder-based stochastic data-driven model that is pre-trained on an imperfect climate model simulation from a 2-layer quasi-geostrophic flow and re-trained, using transfer learning, on a small number of noisy observations from a perfect simulation.

Transfer Learning Weather Forecasting

Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components

no code implementations15 Feb 2021 Alexander Wikner, Jaideep Pathak, Brian R. Hunt, Istvan Szunyogh, Michelle Girvan, Edward Ott

We show that by using partial measurements of the state of the dynamical system, we can train a machine learning model to improve predictions made by an imperfect knowledge-based model.

BIG-bench Machine Learning

Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations

no code implementations30 Sep 2020 Jaideep Pathak, Mustafa Mustafa, Karthik Kashinath, Emmanuel Motheau, Thorsten Kurth, Marcus Day

As a proof-of-concept, we demonstrate our ML-PDE strategy on a two-dimensional turbulent (Rayleigh Number $Ra=10^9$) Rayleigh-B\'enard Convection (RBC) problem.

BIG-bench Machine Learning

Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systems

no code implementations10 Feb 2020 Alexander Wikner, Jaideep Pathak, Brian Hunt, Michelle Girvan, Troy Arcomano, Istvan Szunyogh, Andrew Pomerance, Edward Ott

We consider the commonly encountered situation (e. g., in weather forecasting) where the goal is to predict the time evolution of a large, spatiotemporally chaotic dynamical system when we have access to both time series data of previous system states and an imperfect model of the full system dynamics.

BIG-bench Machine Learning Time Series +1

Backpropagation Algorithms and Reservoir Computing in Recurrent Neural Networks for the Forecasting of Complex Spatiotemporal Dynamics

1 code implementation9 Oct 2019 Pantelis R. Vlachas, Jaideep Pathak, Brian R. Hunt, Themistoklis P. Sapsis, Michelle Girvan, Edward Ott, Petros Koumoutsakos

We examine the efficiency of Recurrent Neural Networks in forecasting the spatiotemporal dynamics of high dimensional and reduced order complex systems using Reservoir Computing (RC) and Backpropagation through time (BPTT) for gated network architectures.

Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model

no code implementations9 Mar 2018 Jaideep Pathak, Alexander Wikner, Rebeckah Fussell, Sarthak Chandra, Brian Hunt, Michelle Girvan, Edward Ott

A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the physical processes governing the dynamics to build an approximate mathematical model of the system.

BIG-bench Machine Learning Time Series +1

Using Machine Learning to Replicate Chaotic Attractors and Calculate Lyapunov Exponents from Data

no code implementations19 Oct 2017 Jaideep Pathak, Zhixin Lu, Brian R. Hunt, Michelle Girvan, Edward Ott

For the case of the KS equation, we note that as the system's spatial size is increased, the number of Lyapunov exponents increases, thus yielding a challenging test of our method, which we find the method successfully passes.

Chaotic Dynamics

Reservoir observers: Model-free inference of unmeasured variables in chaotic systems

no code implementations Chaos 27, 041102 (2017) 2017 Zhixin Lu, Jaideep Pathak, Brian Hunt, Michelle Girvan, Roger Brockett, and Edward Ott

A scheme that accomplishes this is called an “observer.” We consider the case in which a model of the system is unavailable or insufficiently accurate, but “training” time series data of the desired state variables are available for a short period of time, and a limited number of other system variables are continually measured.

Time Series

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