6 citations · 11 across the 9 of their papers we have counts for
9 papers
Rapid Earthquake-to-Tsunami Waveform Generation via Large-Scale Multi-GPU FFT Convolution Applied to the Cascadia Subduction Zone
Bowen Shi, Sreeram Venkat, Stefan Henneking +1
Data-driven methods for earthquake and tsunami early warning rely on large ensembles of rupture scenarios and their resulting waveforms, but generating such datasets with repeated…
Accelerating High-Order Finite Element Simulations at Extreme Scale with FP64 Tensor Cores
Jiqun Tu, Ian Karlin, John Camier +4
Finite element simulations play a critical role in a wide range of applications, from automotive design to tsunami modeling and computational electromagnetics. Performing these sim…
Sensor Placement for Tsunami Early Warning via Large-Scale Bayesian Optimal Experimental Design
Sreeram Venkat, Stefan Henneking, Omar Ghattas
Real-time tsunami early warning relies on distributed sensor networks to infer seismic sources and seafloor motion. Optimizing these networks via Bayesian optimal experimental desi…
Tucker Tensor Train Taylor Series
Nick Alger, Blake Christierson, Peng Chen +1
Learning derivative-accurate surrogates for implicit simulators is a key challenge in scientific machine learning. High-order Taylor surrogates have long been considered intractabl…
Real-time probabilistic tsunami forecasting in Cascadia from sparse offshore pressure observations
Stefan Henneking, Fabian Kutschera, Sreeram Venkat +2
Near-field tsunami early warning in the Cascadia Subduction Zone is limited by sparse offshore observations. We investigate whether a hypothetical network of 175 ocean-bottom press…
Mixed-Precision Performance Portability of FFT-Based GPU-Accelerated Algorithms for Block-Triangular Toeplitz Matrices
Sreeram Venkat, Kasia Swirydowicz, Noah Wolfe +1
The hardware diversity in leadership-class computing facilities, alongside the immense performance boosts from today's GPUs when computing in lower precision, incentivizes scientif…
Real-time Bayesian inference at extreme scale: A digital twin for tsunami early warning applied to the Cascadia subduction zone
Stefan Henneking, Sreeram Venkat, Veselin Dobrev +5
We present a Bayesian inversion-based digital twin that employs acoustic pressure data from seafloor sensors, along with 3D coupled acoustic-gravity wave equations, to infer earthq…
Goal-Oriented Real-Time Bayesian Inference for Linear Autonomous Dynamical Systems With Application to Digital Twins for Tsunami Early Warning
Stefan Henneking, Sreeram Venkat, Omar Ghattas
We present a goal-oriented framework for constructing digital twins with the following properties: (1) they employ discretizations of high-fidelity PDE models governed by autonomou…
Fast And Scalable FFT-Based GPU-Accelerated Algorithms for Block-Triangular Toeplitz Matrices With Application to Linear Inverse Problems Governed by Autonomous Dynamical Systems
Sreeram Venkat, Milinda Fernando, Stefan Henneking +1
We present an efficient and scalable algorithm for performing matrix-vector multiplications ("matvecs") for block Toeplitz matrices. Such matrices, which are shift-invariant with r…