1 code implementation • 3 Oct 2023 • Oliver Watt-Meyer, Gideon Dresdner, Jeremy McGibbon, Spencer K. Clark, Brian Henn, James Duncan, Noah D. Brenowitz, Karthik Kashinath, Michael S. Pritchard, Boris Bonev, Matthew E. Peters, Christopher S. Bretherton
Existing ML-based atmospheric models are not suitable for climate prediction, which requires long-term stability and physical consistency.
no code implementations • 24 Sep 2023 • Morteza Mardani, Noah Brenowitz, Yair Cohen, Jaideep Pathak, Chieh-Yu Chen, Cheng-Chin Liu, Arash Vahdat, Karthik Kashinath, Jan Kautz, Mike Pritchard
Predictions of weather hazard require expensive km-scale simulations driven by coarser global inputs.
no code implementations • 16 Sep 2023 • Torsten Hoefler, Bjorn Stevens, Andreas F. Prein, Johanna Baehr, Thomas Schulthess, Thomas F. Stocker, John Taylor, Daniel Klocke, Pekka Manninen, Piers M. Forster, Tobias Kölling, Nicolas Gruber, Hartwig Anzt, Claudia Frauen, Florian Ziemen, Milan Klöwer, Karthik Kashinath, Christoph Schär, Oliver Fuhrer, Bryan N. Lawrence
Participants of the Berlin Summit on Earth Virtualization Engines (EVEs) discussed ideas and concepts to improve our ability to cope with climate change.
1 code implementation • NeurIPS 2023 • Sungduk Yu, Walter Hannah, Liran Peng, Jerry Lin, Mohamed Aziz Bhouri, Ritwik Gupta, Björn Lütjens, Justus Christopher Will, Gunnar Behrens, Julius Busecke, Nora Loose, Charles I Stern, Tom Beucler, Bryce Harrop, Benjamin R Hillman, Andrea Jenney, Savannah Ferretti, Nana Liu, Anima Anandkumar, Noah D Brenowitz, Veronika Eyring, Nicholas Geneva, Pierre Gentine, Stephan Mandt, Jaideep Pathak, Akshay Subramaniam, Carl Vondrick, Rose Yu, Laure Zanna, Tian Zheng, Ryan Abernathey, Fiaz Ahmed, David C Bader, Pierre Baldi, Elizabeth Barnes, Christopher Bretherton, Peter Caldwell, Wayne Chuang, Yilun Han, Yu Huang, Fernando Iglesias-Suarez, Sanket Jantre, Karthik Kashinath, Marat Khairoutdinov, Thorsten Kurth, Nicholas Lutsko, Po-Lun Ma, Griffin Mooers, J. David Neelin, David Randall, Sara Shamekh, Mark A Taylor, Nathan Urban, Janni Yuval, Guang Zhang, Michael Pritchard
The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators.
2 code implementations • 6 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.
1 code implementation • 25 Apr 2023 • Adam Rupe, Karthik Kashinath, Nalini Kumar, James P. Crutchfield
Spontaneous self-organization is ubiquitous in systems far from thermodynamic equilibrium.
no code implementations • 21 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.
no code implementations • 8 Aug 2022 • Thorsten Kurth, Shashank Subramanian, Peter Harrington, Jaideep Pathak, Morteza Mardani, David Hall, Andrea Miele, Karthik Kashinath, Animashree Anandkumar
Extreme weather amplified by climate change is causing increasingly devastating impacts across the globe.
4 code implementations • 22 Feb 2022 • Jaideep Pathak, Shashank Subramanian, Peter Harrington, Sanjeev Raja, Ashesh Chattopadhyay, Morteza Mardani, Thorsten Kurth, David Hall, Zongyi Li, Kamyar Azizzadenesheli, Pedram Hassanzadeh, Karthik Kashinath, Animashree Anandkumar
FourCastNet accurately forecasts high-resolution, fast-timescale variables such as the surface wind speed, precipitation, and atmospheric water vapor.
1 code implementation • 16 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.
no code implementations • 30 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.
1 code implementation • 1 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.
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.
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.
1 code implementation • 20 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.
2 code implementations • 25 Sep 2019 • Adam Rupe, Nalini Kumar, Vladislav Epifanov, Karthik Kashinath, Oleksandr Pavlyk, Frank Schlimbach, Mostofa Patwary, Sergey Maidanov, Victor Lee, Prabhat, James P. Crutchfield
Extracting actionable insight from complex unlabeled scientific data is an open challenge and key to unlocking data-driven discovery in science.
no code implementations • 16 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.
no code implementations • 13 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.
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.
Ranked #24 on Semantic Segmentation on Stanford2D3D Panoramic