<--- Back to Details
First PageDocument Content
Numerical analysis / Mathematical analysis / Computational fluid dynamics / Computational science / Shock capturing method / Finite volume method / Partial differential equation / Finite difference method / CourantFriedrichsLewy condition / RungeKutta methods / Discretization
Date: 2005-02-08 09:42:14
Numerical analysis
Mathematical analysis
Computational fluid dynamics
Computational science
Shock capturing method
Finite volume method
Partial differential equation
Finite difference method
CourantFriedrichsLewy condition
RungeKutta methods
Discretization

Staggered Finite Difference Schemes for Balance Laws Gabriella Puppo and Giovanni Russo Abstract. In this paper a new family of high-order finite-difference shock-capturing central schemes for hyperbolic systems with sti

Add to Reading List

Source URL: www.dmi.unict.it

Download Document from Source Website

File Size: 342,34 KB

Share Document on Facebook

Similar Documents

MathQuest: Differential Equations Introduction to Partial Differential Equations 1. Which of the following functions satisfies the equation x ∂f + y ∂f = f? ∂x

MathQuest: Differential Equations Introduction to Partial Differential Equations 1. Which of the following functions satisfies the equation x ∂f + y ∂f = f? ∂x

DocID: 1vb7j - View Document

Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control Yangchen Pan 1 2 Amir-massoud Farahmand 3 2 Martha White 1 Saleh Nabi 2 Piyush Grover 2 Daniel Nikovski 2

Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control Yangchen Pan 1 2 Amir-massoud Farahmand 3 2 Martha White 1 Saleh Nabi 2 Piyush Grover 2 Daniel Nikovski 2

DocID: 1uYik - View Document

Deep Reinforcement Learning for Partial Differential Equation Control Amir-massoud Farahmand, Saleh Nabi, Daniel N. Nikovski Abstract— This paper develops a data-driven method for control of partial differential equati

Deep Reinforcement Learning for Partial Differential Equation Control Amir-massoud Farahmand, Saleh Nabi, Daniel N. Nikovski Abstract— This paper develops a data-driven method for control of partial differential equati

DocID: 1uLR0 - View Document

MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com Deep Reinforcement Learning for Partial Differential Equation Control Farahmand, A.-M.; Nabi, S.; Nikovski, D.N.

MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com Deep Reinforcement Learning for Partial Differential Equation Control Farahmand, A.-M.; Nabi, S.; Nikovski, D.N.

DocID: 1untp - View Document

Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control Yangchen Pan 1 2 Amir-massoud Farahmand 3 2 Martha White 1 Saleh Nabi 2 Piyush Grover 2 Daniel Nikovski 2

Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control Yangchen Pan 1 2 Amir-massoud Farahmand 3 2 Martha White 1 Saleh Nabi 2 Piyush Grover 2 Daniel Nikovski 2

DocID: 1ujg3 - View Document