Designing Accurate Emulators for Scientific Processes using
Calibration-Driven Deep Models

Designing Accurate Emulators for Scientific Processes using Calibration-Driven Deep Models

5 May 2020

Jayaraman J. Thiagarajan

Bindya Venkatesh

Papers citing "Designing Accurate Emulators for Scientific Processes using Calibration-Driven Deep Models"

10 / 10 papers shown

Title
A Spatio-Temporal Neural Network Forecasting Approach for Emulation of Firefront Models Andrew Bolt C. Huston Petra Kuhnert J. Dabrowski J. Hilton Conrad Sanderson AI4TS 26 11 0 17 Jun 2022
Self-learning Emulators and Eigenvector Continuation Avik Sarkar Dean Lee 14 17 0 28 Jul 2021
Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers A. Chattopadhyay M. Mustafa P. Hassanzadeh Eviatar Bach K. Kashinath AI4CE 16 25 0 16 Mar 2021
Lagrangian Neural Networks M. Cranmer S. Greydanus Stephan Hoyer Peter W. Battaglia D. Spergel S. Ho PINN 136 424 0 10 Mar 2020
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 341 11,684 0 09 Mar 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 257 3,684 0 28 Feb 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,661 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,138 0 06 Jun 2015
Sparse/Robust Estimation and Kalman Smoothing with Nonsmooth Log-Concave Densities: Modeling, Computation, and Theory Aleksandr Aravkin J. Burke G. Pillonetto 84 64 0 19 Jan 2013