Results 21 to 30 of about 5,645 (242)
On the adaptive selection of the parameter in stabilized finite element approximations [PDF]
, 2013 A systematic approach is developed for the selection of the stabilization parameter for stabilized finite element approximation of the Stokes problem, whereby the parameter is chosen to minimize a computable upper bound for the error in the approximation.
Rankin, Richard Andrew Robert +3 more
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Numerical simulation of tridimensional electromagnetic shaping of liquid metals [PDF]
, 1992 We describe a numerical method to compute free surfaces in electomagnetic shaping and levitation of liquid metals. We use an energetic variational formulation based on the total energy and we apply optimization techniques to compute a critical point. The
Pierre, Michel, Roche, Jean, R.
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Modeling, Simulation, and Design of Self-Assembling Space Systems: Accurate Collision Detection, Robust Time Integration, and Optimal Control [PDF]
, 2013 Motivated by issues inherent in modeling and designing self-assembling systems (e.g. multiple collisions, collisions between non-smooth bodies, clumping and jamming behaviors, etc.), the goal of this thesis is to develop robust numerical tools that ...
Gwendolyn B. Johnson +1 more
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IEEE Access, 2019 Although the hybrid analog–digital processing reduces the hardware complexity to realize massive multiple-input multiple-output system, it increases the difficulty of channel estimation significantly since the base station cannot obtain enough ...
Yurong Wang +3 more
doaj +1 more source
On convergence of the maximum block improvement method [PDF]
, 2015 . The MBI (maximum block improvement) method is a greedy approach to solving optimization problems where the decision variables can be grouped into a finite number of blocks. Assuming that optimizing over one block of variables while fixing all others is
Shuzhong Zhang +5 more
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Fast iterative solvers for PDE-constrained optimization problems [PDF]
, 2013 In this thesis, we develop preconditioned iterative methods for the solution of matrix systems arising from PDE-constrained optimization problems. In order to do this, we exploit saddle point theory, as this is the form of the matrix systems we wish to ...
Pearson, John W
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Differentiable Programming Tensor Networks
Physical Review X, 2019 Differentiable programming is a fresh programming paradigm which composes parameterized algorithmic components and optimizes them using gradient search. The concept emerges from deep learning but is not limited to training neural networks. We present the
Hai-Jun Liao +3 more
doaj +1 more source
Single-step deep reinforcement learning for two- and three-dimensional optimal shape design
AIP Advances, 2022 This research gauges the capabilities of deep reinforcement learning (DRL) techniques for direct optimal shape design in computational fluid dynamics (CFD) systems.
H. Ghraieb +4 more
doaj +1 more source
Allen-Cahn and Cahn-Hilliard variational inequalities solved with Optimization Techniques [PDF]
, 2010 Parabolic variational inequalities of Allen-Cahn and Cahn- Hilliard type are solved using methods involving constrained optimization. Time discrete variants are formulated with the help of Lagrange multipliers for local and non-local equality and ...
Garcke, Harald +4 more
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New trends in general variational inequalities [PDF]
, 2020 It is well known that general variational inequalities provide us with a unified, natural, novel and simple framework to study a wide class of unrelated problems, which arise in pure and applied sciences.
Rassias, Michael Th +5 more
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