Andrian Uihlein
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View article: A Stochastic Method of Moving Asymptotes for Topology Optimization Under Uncertainty
A Stochastic Method of Moving Asymptotes for Topology Optimization Under Uncertainty Open
Topology optimization under uncertainty or reliability‐based topology optimization is usually numerically very expensive. This is mainly due to the fact that an accurate evaluation of the probabilistic model requires the system to be simul…
View article: A 140 line MATLAB code for topology optimization problems with probabilistic parameters
A 140 line MATLAB code for topology optimization problems with probabilistic parameters Open
We present an efficient 140 line MATLAB code for topology optimization problems that include probabilistic parameters. It is built from the top99neo code by Ferrari and Sigmund and incorporates a stochastic sample-based approach. Old gradi…
View article: Predictive design of plasmonic color
Predictive design of plasmonic color Open
Predictive design promises direct access to nanomaterials with optimal properties, identified by rigorous mathematical optimization. This strategy thus provides structural blueprints for synthesis and circumvents extensive parameter screen…
View article: A stochastic method of moving asymptotes for topology optimization under uncertainty
A stochastic method of moving asymptotes for topology optimization under uncertainty Open
Topology optimization under uncertainty or reliability-based topology optimization is usually numerically very expensive. This is mainly due to the fact that an accurate evaluation of the probabilistic model requires the system to be simul…
View article: Topology optimization of broadband acoustic transition section: a comparison between deterministic and stochastic approaches
Topology optimization of broadband acoustic transition section: a comparison between deterministic and stochastic approaches Open
This paper focuses on the topology optimization of a broadband acoustic transition section that connects two cylindrical waveguides with different radii. The primary objective is to design a transition section that maximizes the transmissi…
View article: Stabilized SQP Methods in Hilbert Spaces
Stabilized SQP Methods in Hilbert Spaces Open
Based on techniques by (S.J. Wright 1998) for finite-dimensional optimization, we investigate a stabilized sequential quadratic programming method for nonlinear optimization problems in infinite-dimensional Hilbert spaces. The method is sh…
View article: Targeted color design of silver–gold alloy nanoparticles
Targeted color design of silver–gold alloy nanoparticles Open
Mathematical, data-driven optimization of a green synthesis route for silver–gold alloy nanoparticles, controlling optical properties without a known formation mechanism.
View article: The continuous stochastic gradient method: part I–convergence theory
The continuous stochastic gradient method: part I–convergence theory Open
In this contribution, we present a full overview of the continuous stochastic gradient (CSG) method, including convergence results, step size rules and algorithmic insights. We consider optimization problems in which the objective function…
View article: The Continuous Stochastic Gradient Method
The Continuous Stochastic Gradient Method Open
of the underlying manuscript (https://doi.org/10.48550/arXiv.2303.12477): In this contribution, we present a numerical analysis of the continuous stochastic gradient (CSG) method, including applications from topology optimization and conve…
View article: The Continuous Stochastic Gradient Method
The Continuous Stochastic Gradient Method Open
of the underlying manuscript (https://doi.org/10.48550/arXiv.2303.12477):In this contribution, we present a numerical analysis of the continuous stochastic gradient (CSG) method, including applications from topology optimization and conver…
View article: Topology Optimization of Broadband Acoustic Transition Section: A Comparison between Deterministic and Stochastic Approaches
Topology Optimization of Broadband Acoustic Transition Section: A Comparison between Deterministic and Stochastic Approaches Open
This paper focuses on the topology optimization of a broadband acoustic transition section that connects two cylindrical waveguides with different radii. The primary objective is to design a transition section such that it maximizes the tr…
View article: Targeted color design of silver-gold alloy nanoparticles
Targeted color design of silver-gold alloy nanoparticles Open
This is the raw data for the manuscript: Targeted color design of silver-gold alloy nanoparticlesAbstract:This research article focuses on the targeted color design of silver-gold alloy nanoparticles (NPs), employing a multivariate optimiz…
View article: Targeted color design of silver-gold alloy nanoparticles
Targeted color design of silver-gold alloy nanoparticles Open
This is the raw data for the manuscript: Targeted color design of silver-gold alloy nanoparticlesAbstract:This research article focuses on the targeted color design of silver-gold alloy nanoparticles (NPs), employing a multivariate optimiz…
View article: The Continuous Stochastic Gradient Method: Part II -- Application and Numerics
The Continuous Stochastic Gradient Method: Part II -- Application and Numerics Open
In this contribution, we present a numerical analysis of the continuous stochastic gradient (CSG) method, including applications from topology optimization and convergence rates. In contrast to standard stochastic gradient optimization sch…
View article: Optimizing Color of Particulate Products
Optimizing Color of Particulate Products Open
Optical properties of nanoparticles largely depend on their shape and material distribution. Given any such property, e.g., a desired color, the aim is to design an optimal nanoparticle, whose properties best match these targets. The corre…
View article: The Continuous Stochastic Gradient Method: Part I -- Convergence Theory
The Continuous Stochastic Gradient Method: Part I -- Convergence Theory Open
In this contribution, we present a full overview of the continuous stochastic gradient (CSG) method, including convergence results, step size rules and algorithmic insights. We consider optimization problems in which the objective function…
View article: CSG: A stochastic gradient method for a wide class of optimization problems appearing in a machine learning or data-driven context
CSG: A stochastic gradient method for a wide class of optimization problems appearing in a machine learning or data-driven context Open
A recent article introduced thecontinuous stochastic gradient method (CSG) for the efficient solution of a class of stochastic optimization problems. While the applicability of known stochastic gradient type methods is typically limited to…