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Derivation of Dirac equation from the stochastic optimal control principles of quantum mechanics. [PDF]
Yordanov V.
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Bridge, Reverse Bridge, and Their Control. [PDF]
Baldassarri A, Puglisi A.
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Delay-aware chemotherapy dosing via online critic learning. [PDF]
Rahimi F, Samadi M.
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Solving nonlinear and complex optimal control problems via multi-task artificial neural networks. [PDF]
Kerdabadi AE, Malek A.
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In-Orbit Optimal Safe Formation Control for Surrounding an Unknown Huge Target with Specific Structure by Using Relative Sensors Only. [PDF]
Wei B, Li C, Dang Z, Yue X.
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Research on enterprise network public opinion guiding decision-making considering crisis differentiation. [PDF]
Wang J, Li Y, Guo X, Chen H.
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Hamilton–Jacobi–Bellman Equations
2017In this chapter we present recent developments in the theory of Hamilton–Jacobi–Bellman (HJB) equations as well as applications. The intention of this chapter is to exhibit novel methods and techniques introduced few years ago in order to solve long-standing questions in nonlinear optimal control theory of Ordinary Differential Equations (ODEs).
Festa, Adriano +6 more
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2001
We already know that canonical transformations are useful for solving mechanical problems. We now want to look for a canonical transformation that transforms the 2N coordinates (q i , p i ) to 2N constant values (Q i , P i ), e.g., to the 2N initial values \((q_{i}^{0},p_{i}^{0})\) at time t = 0.
Walter Dittrich, Martin Reuter
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We already know that canonical transformations are useful for solving mechanical problems. We now want to look for a canonical transformation that transforms the 2N coordinates (q i , p i ) to 2N constant values (Q i , P i ), e.g., to the 2N initial values \((q_{i}^{0},p_{i}^{0})\) at time t = 0.
Walter Dittrich, Martin Reuter
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Relaxation of Hamilton-Jacobi Equations
Archive for Rational Mechanics and Analysis, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hitoshi Ishii, LORETI, Paola
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Stochastic Hamilton–Jacobi–Bellman Equations
SIAM Journal on Control and Optimization, 1992Summary: This paper studies the following form of nonlinear stochastic partial differential equation: \[ \begin{multlined} -d\Phi_ t=\inf_{v\in U}\left\{\frac12 \sum_{i,j}[\sigma\sigma^*]_{ij}(x,v,t)\partial_{x_ ix_ j}\Phi_ t(x)+\sum_ i b_ i(x,v,t)\partial_{x_ i}\Phi_ t(x)+L(x,v,t)+\right. \\ \left.+\sum_{i,j}\sigma_{ij}(x,v,t)\partial _{x_ i}\Psi_{j,t}
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