ISOPOD: InverSe OPtimizatiOn and Design

INL Software; Zachary Prince; Lynn Munday; Dewen Yushu; Daniel Schwen

ISOPOD: InverSe OPtimizatiOn and Design

INL Software (Photographer)
, Zachary Prince (Developer)
, Lynn Munday (Developer)
, Dewen Yushu (Developer)
, Daniel Schwen (Developer)

Research output: Non-textual form › Software

Abstract

ISOPOD (InverSe OPtimizatiOn and Design) will be a MOOSE (Multiphysics Object Oriented Simulation Environment)-based framework which will iteratively estimate the parameters by minimizing the difference between simulated and experimental observables. While inverse optimization has long been used for parameter estimation, the unique feature of ISOPOD is rapid solution of new inverse problems, leading to accelerated Research, Development and Demonstration (RD&D) for complex parameter estimation problems. This will be achieved by extending the software structure of MOOSE to inverse optimization, as well as building on the unique automatic differentiation feature of MOOSE.

This software is open source and available at no cost.

Original language	American English
State	Published - 2021

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title = "ISOPOD: InverSe OPtimizatiOn and Design",

abstract = "ISOPOD (InverSe OPtimizatiOn and Design) will be a MOOSE (Multiphysics Object Oriented Simulation Environment)-based framework which will iteratively estimate the parameters by minimizing the difference between simulated and experimental observables. While inverse optimization has long been used for parameter estimation, the unique feature of ISOPOD is rapid solution of new inverse problems, leading to accelerated Research, Development and Demonstration (RD\&D) for complex parameter estimation problems. This will be achieved by extending the software structure of MOOSE to inverse optimization, as well as building on the unique automatic differentiation feature of MOOSE. This software is open source and available at no cost.",

author = "\{INL Software\} and Zachary Prince and Lynn Munday and Dewen Yushu and Daniel Schwen",

year = "2021",

language = "American English",

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A2 - Schwen, Daniel

PY - 2021

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N2 - ISOPOD (InverSe OPtimizatiOn and Design) will be a MOOSE (Multiphysics Object Oriented Simulation Environment)-based framework which will iteratively estimate the parameters by minimizing the difference between simulated and experimental observables. While inverse optimization has long been used for parameter estimation, the unique feature of ISOPOD is rapid solution of new inverse problems, leading to accelerated Research, Development and Demonstration (RD&D) for complex parameter estimation problems. This will be achieved by extending the software structure of MOOSE to inverse optimization, as well as building on the unique automatic differentiation feature of MOOSE. This software is open source and available at no cost.

AB - ISOPOD (InverSe OPtimizatiOn and Design) will be a MOOSE (Multiphysics Object Oriented Simulation Environment)-based framework which will iteratively estimate the parameters by minimizing the difference between simulated and experimental observables. While inverse optimization has long been used for parameter estimation, the unique feature of ISOPOD is rapid solution of new inverse problems, leading to accelerated Research, Development and Demonstration (RD&D) for complex parameter estimation problems. This will be achieved by extending the software structure of MOOSE to inverse optimization, as well as building on the unique automatic differentiation feature of MOOSE. This software is open source and available at no cost.

M3 - Software

ER -

ISOPOD: InverSe OPtimizatiOn and Design

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