7,873 research outputs found
Optimizing Photonic Nanostructures via Multi-fidelity Gaussian Processes
We apply numerical methods in combination with finite-difference-time-domain
(FDTD) simulations to optimize transmission properties of plasmonic mirror
color filters using a multi-objective figure of merit over a five-dimensional
parameter space by utilizing novel multi-fidelity Gaussian processes approach.
We compare these results with conventional derivative-free global search
algorithms, such as (single-fidelity) Gaussian Processes optimization scheme,
and Particle Swarm Optimization---a commonly used method in nanophotonics
community, which is implemented in Lumerical commercial photonics software. We
demonstrate the performance of various numerical optimization approaches on
several pre-collected real-world datasets and show that by properly trading off
expensive information sources with cheap simulations, one can more effectively
optimize the transmission properties with a fixed budget.Comment: NIPS 2018 Workshop on Machine Learning for Molecules and Materials.
arXiv admin note: substantial text overlap with arXiv:1811.0075
Multi-Information Source Fusion and Optimization to Realize ICME: Application to Dual Phase Materials
Integrated Computational Materials Engineering (ICME) calls for the
integration of computational tools into the materials and parts development
cycle, while the Materials Genome Initiative (MGI) calls for the acceleration
of the materials development cycle through the combination of experiments,
simulation, and data. As they stand, both ICME and MGI do not prescribe how to
achieve the necessary tool integration or how to efficiently exploit the
computational tools, in combination with experiments, to accelerate the
development of new materials and materials systems. This paper addresses the
first issue by putting forward a framework for the fusion of information that
exploits correlations among sources/models and between the sources and `ground
truth'. The second issue is addressed through a multi-information source
optimization framework that identifies, given current knowledge, the next best
information source to query and where in the input space to query it via a
novel value-gradient policy. The querying decision takes into account the
ability to learn correlations between information sources, the resource cost of
querying an information source, and what a query is expected to provide in
terms of improvement over the current state. The framework is demonstrated on
the optimization of a dual-phase steel to maximize its strength-normalized
strain hardening rate. The ground truth is represented by a
microstructure-based finite element model while three low fidelity information
sources---i.e. reduced order models---based on different homogenization
assumptions---isostrain, isostress and isowork---are used to efficiently and
optimally query the materials design space.Comment: 19 pages, 11 figures, 5 table
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