Search Results for author: Karthik Kashinath

Found 19 papers, 11 papers with code

Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere

2 code implementations6 Jun 2023 Boris Bonev, Thorsten Kurth, Christian Hundt, Jaideep Pathak, Maximilian Baust, Karthik Kashinath, Anima Anandkumar

Fourier Neural Operators (FNOs) have proven to be an efficient and effective method for resolution-independent operator learning in a broad variety of application areas across scientific machine learning.

Operator learning

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

Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers

1 code implementation16 Mar 2021 Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Eviatar Bach, Karthik Kashinath

These components are 1) a deep spatial transformer added to the latent space of the U-NETs to preserve a property called equivariance, which is related to correctly capturing rotations and scalings of features in spatio-temporal data, 2) a data-assimilation (DA) algorithm to ingest noisy observations and improve the initial conditions for next forecasts, and 3) a multi-time-step algorithm, which combines forecasts from DDWP models with different time steps through DA, improving the accuracy of forecasts at short intervals.

Weather Forecasting

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

MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework

1 code implementation1 May 2020 Chiyu Max Jiang, Soheil Esmaeilzadeh, Kamyar Azizzadenesheli, Karthik Kashinath, Mustafa Mustafa, Hamdi A. Tchelepi, Philip Marcus, Prabhat, Anima Anandkumar

We propose MeshfreeFlowNet, a novel deep learning-based super-resolution framework to generate continuous (grid-free) spatio-temporal solutions from the low-resolution inputs.

Super-Resolution

Enforcing Physical Constraints in CNNs through Differentiable PDE Layer

no code implementations ICLR Workshop DeepDiffEq 2019 Chiyu "Max" Jiang, Karthik Kashinath, Prabhat, Philip Marcus

To this end, we propose the use of a novel differentiable spectral projection layer for neural networks that efficiently enforces spatial PDE constraints using spectral methods, yet is fully differentiable, allowing for its use as a layer within Convolutional Neural Networks (CNNs) during end-to-end training.

Generative Adversarial Network

Enforcing Physical Constraints in Neural Neural Networks through Differentiable PDE Layer

1 code implementation ICLR 2020 Chiyu "Max" Jiang, Karthik Kashinath, Prabhat, Philip Marcus

To this end, we propose the use of a novel differentiable spectral projection layer for neural networks that efficiently enforces spatial PDE constraints using spectral methods, yet is fully differentiable, allowing for its use as a layer in neural networks that supports end-to-end training.

Generative Adversarial Network Super-Resolution

Towards Physics-informed Deep Learning for Turbulent Flow Prediction

1 code implementation20 Nov 2019 Rui Wang, Karthik Kashinath, Mustafa Mustafa, Adrian Albert, Rose Yu

While deep learning has shown tremendous success in a wide range of domains, it remains a grand challenge to incorporate physical principles in a systematic manner to the design, training, and inference of such models.

Towards Unsupervised Segmentation of Extreme Weather Events

no code implementations16 Sep 2019 Adam Rupe, Karthik Kashinath, Nalini Kumar, Victor Lee, Prabhat, James P. Crutchfield

Extreme weather is one of the main mechanisms through which climate change will directly impact human society.

Representation Learning

Enforcing Statistical Constraints in Generative Adversarial Networks for Modeling Chaotic Dynamical Systems

no code implementations13 May 2019 Jin-Long Wu, Karthik Kashinath, Adrian Albert, Dragos Chirila, Prabhat, Heng Xiao

In this work, we present a statistical constrained generative adversarial network by enforcing constraints of covariance from the training data, which results in an improved machine-learning-based emulator to capture the statistics of the training data generated by solving fully resolved PDEs.

Generative Adversarial Network

Spherical CNNs on Unstructured Grids

1 code implementation ICLR 2019 Chiyu "Max" Jiang, Jingwei Huang, Karthik Kashinath, Prabhat, Philip Marcus, Matthias Niessner

We present an efficient convolution kernel for Convolutional Neural Networks (CNNs) on unstructured grids using parameterized differential operators while focusing on spherical signals such as panorama images or planetary signals.

Semantic Segmentation

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