455 citations · 711 across the 25 of their papers we have counts for
26 papers
Anomalous Diffusion of Tropical Cyclones Observed in Huge Ensembles of Hindcasts
Abdoul R. Zeba, William D. Collins, Ankur Mahesh +5
We examine whether tropical cyclones (TCs) obey ordinary Brownian or anomalous diffusion using a huge ensemble (HENS) of hindcasts for summer 2023. Anomalous diffusion has been inf…
Surface temperature extremes produced by huge machine learning hindcasts of summer 2023
Mark Risser, Ankur Mahesh, William D. Collins +6
The summer of 2023 brought record-breaking heat extremes across the globe. In this work, we investigate how much worse these extremes might have become, given identical large-scale…
On Neural Scaling Laws for Weather Emulation through Continual Training
Shashank Subramanian, Alexander Kiefer, Arnur Nigmetov +3
Neural scaling laws, which in some domains can predict the performance of large neural networks as a function of model, data, and compute scale, are the cornerstone of building fou…
Examining Fast Radiatively Driven Responses Using Machine-Learning Weather Emulators
Ankur Mahesh, William D. Collins, Travis A. O'Brien +10
The response of the climate system to increased greenhouse gases and other radiative perturbations is governed by a combination of fast and slow feedbacks. Slow feedbacks are typic…
Comprehensive Performance Modeling and System Design Insights for Foundation Models
Shashank Subramanian, Ermal Rrapaj, Peter Harrington +6
Generative AI, in particular large transformer models, are increasingly driving HPC system design in science and industry. We analyze performance characteristics of such transforme…
Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operators
Ankur Mahesh, William Collins, Boris Bonev +13
Studying low-likelihood high-impact extreme weather events in a warming world is a significant and challenging task for current ensemble forecasting systems. While these systems pr…
Huge Ensembles Part II: Properties of a Huge Ensemble of Hindcasts Generated with Spherical Fourier Neural Operators
Ankur Mahesh, William Collins, Boris Bonev +12
In Part I, we created an ensemble based on Spherical Fourier Neural Operators. As initial condition perturbations, we used bred vectors, and as model perturbations, we used multipl…
Analyzing and Exploring Training Recipes for Large-Scale Transformer-Based Weather Prediction
Jared D. Willard, Peter Harrington, Shashank Subramanian +3
The rapid rise of deep learning (DL) in numerical weather prediction (NWP) has led to a proliferation of models which forecast atmospheric variables with comparable or superior ski…
Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning
Wuyang Chen, Jialin Song, Pu Ren +3
Recent years have witnessed the promise of coupling machine learning methods and physical domain-specific insights for solving scientific problems based on partial differential equ…
SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine Learning
Pu Ren, N. Benjamin Erichson, Junyi Guo +4
Super-resolution (SR) techniques aim to enhance data resolution, enabling the retrieval of finer details, and improving the overall quality and fidelity of the data representation.…
Towards Stability of Autoregressive Neural Operators
Michael McCabe, Peter Harrington, Shashank Subramanian +1
Neural operators have proven to be a promising approach for modeling spatiotemporal systems in the physical sciences. However, training these models for large systems can be quite…
Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior
Shashank Subramanian, Peter Harrington, Kurt Keutzer +4
Pre-trained machine learning (ML) models have shown great performance for a wide range of applications, in particular in natural language processing (NLP) and computer vision (CV).…
Generative Modeling of High-resolution Global Precipitation Forecasts
James Duncan, Shashank Subramanian, Peter Harrington
Forecasting global precipitation patterns and, in particular, extreme precipitation events is of critical importance to preparing for and adapting to climate change. Making accurat…
FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators
Thorsten Kurth, Shashank Subramanian, Peter Harrington +6
Extreme weather amplified by climate change is causing increasingly devastating impacts across the globe. The current use of physics-based numerical weather prediction (NWP) limits…
Adaptive Self-supervision Algorithms for Physics-informed Neural Networks
Shashank Subramanian, Robert M. Kirby, Michael W. Mahoney +1
Physics-informed neural networks (PINNs) incorporate physical knowledge from the problem domain as a soft constraint on the loss function, but recent work has shown that this can l…
FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
Jaideep Pathak, Shashank Subramanian, Peter Harrington +10
FourCastNet, short for Fourier Forecasting Neural Network, is a global data-driven weather forecasting model that provides accurate short to medium-range global predictions at $0.2…
Ensemble inversion for brain tumor growth models with mass effect
Shashank Subramanian, Klaudius Scheufele, Naveen Himthani +2
We propose a method for extracting physics-based biomarkers from a single multiparametric Magnetic Resonance Imaging (mpMRI) scan bearing a glioma tumor. We account for mass effect…
Quantitative in vivo imaging to enable tumor forecasting and treatment optimization
Guillermo Lorenzo, David A. Hormuth, Angela M. Jarrett +6
Current clinical decision-making in oncology relies on averages of large patient populations to both assess tumor status and treatment outcomes. However, cancers exhibit an inheren…
Calibration of Biophysical Models for tau-Protein Spreading in Alzheimer's Disease from PET-MRI
Klaudius Scheufele, Shashank Subramanian, George Biros
Aggregates of misfolded tau proteins (or just 'tau' for brevity) play a crucial role in the progression of Alzheimer's disease (AD) as they correlate with cell death and accelerate…
Multiatlas Calibration of Biophysical Brain Tumor Growth Models with Mass Effect
Shashank Subramanian, Klaudius Scheufele, Naveen Himthani +1
We present a 3D fully-automatic method for the calibration of partial differential equation (PDE) models of glioblastoma (GBM) growth with mass effect, the deformation of brain tis…
Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology
Andreas Mang, Spyridon Bakas, Shashank Subramanian +2
Central nervous system (CNS) tumors come with the vastly heterogeneous histologic, molecular and radiographic landscape, rendering their precise characterization challenging. The r…
Automatic MRI-Driven Model Calibration for Advanced Brain Tumor Progression Analysis
Klaudius Scheufele, Shashank Subramanian, George Biros
Our objective is the calibration of mathematical tumor growth models from a single multiparametric scan. The target problem is the analysis of preoperative Glioblastoma (GBM) scans…
Image-Driven Biophysical Tumor Growth Model Calibration
Klaudius Scheufele, Shashank Subramanian, Andreas Mang +2
We present a novel formulation for the calibration of a biophysical tumor growth model from a single-time snapshot, MRI scan of a glioblastoma patient. Tumor growth models are typi…
Where did the tumor start? An inverse solver with sparse localization for tumor growth models
Shashank Subramanian, Klaudius Scheufele, Miriam Mehl +1
We present a numerical scheme for solving an inverse problem for parameter estimation in tumor growth models for glioblastomas, a form of aggressive primary brain tumor. The growth…