Stability Analyses of Nonuniform Time-Step LOD-FDTD Methods for Electromagnetic and Thermal Simulations

2017 ◽  
Vol 2 ◽  
pp. 183-193 ◽  
Author(s):  
Eng Leong Tan ◽  
Ding Yu Heh
Keyword(s):  
Time Step ◽  
Stability Analyses ◽  
Thermal Simulations ◽  
Fdtd Methods

GPU-Accelerated Efficient Implementation of FDTD Methods With Optimum Time-Step Selection

2014 ◽  
Vol 50 (2) ◽  
pp. 477-480 ◽  
Author(s):  
Theodoros T. Zygiridis ◽  
Nikolaos V. Kantartzis ◽  
Theodoros D. Tsiboukis
Keyword(s):  
Efficient Implementation ◽  
Time Step ◽  
Optimum Time ◽  
Step Selection ◽  
Fdtd Methods

Conformal FDTD-methods to avoid time step reduction with and without cell enlargement

2007 ◽  
Vol 225 (2) ◽  
pp. 1493-1507 ◽  
Author(s):  
Igor Zagorodnov ◽  
Rolf Schuhmann ◽  
Thomas Weiland
Keyword(s):  
Cell Enlargement ◽  
Time Step ◽  
Fdtd Methods

Toward a neuroscope: An application of high-performance computing for real-time evaluation of brain function using MRI

1994 ◽  
Vol 52 ◽  
pp. 922-923
Author(s):  
C. S. Potter ◽  
C. D. Gregory ◽  
H. D. Morris ◽  
Z.-P. Liang ◽  
P. C. Lauterbur
Keyword(s):  
Human Brain ◽  
Brain Function ◽  
High Performance ◽  
Complex Signal ◽  
Time Step ◽  
Brain Organization ◽  
Cognitive Studies ◽  
Positron Emission ◽  
Imaging And Spectroscopy ◽  
3D Anatomy

Over the past few years, several laboratories have demonstrated that changes in local neuronal activity associated with human brain function can be detected by magnetic resonance imaging and spectroscopy. Using these methods, the effects of sensory and motor stimulation have been observed and cognitive studies have begun. These new methods promise to make possible even more rapid and extensive studies of brain organization and responses than those now in use, such as positron emission tomography.Human brain studies are enormously complex. Signal changes on the order of a few percent must be detected against the background of the complex 3D anatomy of the human brain. Today, most functional MR experiments are performed using several 2D slice images acquired at each time step or stimulation condition of the experimental protocol. It is generally believed that true 3D experiments must be performed for many cognitive experiments. To provide adequate resolution, this requires that data must be acquired faster and/or more efficiently to support 3D functional analysis.

Efficient Treatment of Fixed and Induced Dipolar Interactions

2000 ◽  
Vol 653 ◽  
Author(s):  
Celeste Sagui ◽  
Thoma Darden
Keyword(s):  
Molecular Dynamics ◽  
Md Simulations ◽  
Lagrangian Formalism ◽  
Dipolar Interactions ◽  
Time Step ◽  
Efficient Treatment ◽  
Particle Mesh Ewald ◽  
Fixed Charges ◽  
Self Consistency ◽  
Induced Dipoles

AbstractFixed and induced point dipoles have been implemented in the Ewald and Particle-Mesh Ewald (PME) formalisms. During molecular dynamics (MD) the induced dipoles can be propagated along with the atomic positions either by interation to self-consistency at each time step, or by a Car-Parrinello (CP) technique using an extended Lagrangian formalism. The use of PME for electrostatics of fixed charges and induced dipoles together with a CP treatment of dipole propagation in MD simulations leads to a cost overhead of only 33% above that of MD simulations using standard PME with fixed charges, allowing the study of polarizability in largemacromolecular systems.

Teaching AI Agents Ethical Values Using Reinforcement Learning and Policy Orchestration

2019 ◽  
Author(s):  
Ritesh Noothigattu ◽  
Djallel Bouneffouf ◽  
Nicholas Mattei ◽  
Rachita Chandra ◽  
Piyush Madan ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Ethical Values ◽  
Large Role ◽  
Learning To Learn ◽  
Inverse Reinforcement Learning ◽  
Time Step ◽  
Novel Approach

Autonomous cyber-physical agents play an increasingly large role in our lives. To ensure that they behave in ways aligned with the values of society, we must develop techniques that allow these agents to not only maximize their reward in an environment, but also to learn and follow the implicit constraints of society. We detail a novel approach that uses inverse reinforcement learning to learn a set of unspecified constraints from demonstrations and reinforcement learning to learn to maximize environmental rewards. A contextual bandit-based orchestrator then picks between the two policies: constraint-based and environment reward-based. The contextual bandit orchestrator allows the agent to mix policies in novel ways, taking the best actions from either a reward-maximizing or constrained policy. In addition, the orchestrator is transparent on which policy is being employed at each time step. We test our algorithms using Pac-Man and show that the agent is able to learn to act optimally, act within the demonstrated constraints, and mix these two functions in complex ways.

Sign in / Sign up

Export Citation Format

Share Document