[HTML][HTML] Gflownets for ai-driven scientific discovery
Tackling the most pressing problems for humanity, such as the climate crisis and the threat
of global pandemics, requires accelerating the pace of scientific discovery. While science …
of global pandemics, requires accelerating the pace of scientific discovery. While science …
Causal machine learning for single-cell genomics
Advances in single-cell omics allow for unprecedented insights into the transcription profiles
of individual cells. When combined with large-scale perturbation screens, through which …
of individual cells. When combined with large-scale perturbation screens, through which …
A study of Bayesian neural network surrogates for Bayesian optimization
Bayesian optimization is a highly efficient approach to optimizing objective functions which
are expensive to query. These objectives are typically represented by Gaussian process …
are expensive to query. These objectives are typically represented by Gaussian process …
Pre-trained Gaussian processes for Bayesian optimization
Bayesian optimization (BO) has become a popular strategy for global optimization of
expensive real-world functions. Contrary to a common expectation that BO is suited to …
expensive real-world functions. Contrary to a common expectation that BO is suited to …
Automated discovery and optimization of 3D topological photonic crystals
Topological photonic crystals have received considerable attention for their ability to
manipulate and guide light in unique ways. They are typically designed by hand based on …
manipulate and guide light in unique ways. They are typically designed by hand based on …
Datasets and benchmarks for nanophotonic structure and parametric design simulations
Nanophotonic structures have versatile applications including solar cells, anti-reflective
coatings, electromagnetic interference shielding, optical filters, and light emitting diodes. To …
coatings, electromagnetic interference shielding, optical filters, and light emitting diodes. To …
A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?
Automation is one of the cornerstones of contemporary material discovery. Bayesian
optimization (BO) is an essential part of such workflows, enabling scientists to leverage prior …
optimization (BO) is an essential part of such workflows, enabling scientists to leverage prior …
Self-design of arbitrary polarization-control waveplates via deep neural networks
Z Liu, Z Dang, Z Liu, Y Li, X He, Y Dai, Y Chen… - Photonics …, 2023 - opg.optica.org
The manipulation of polarization states beyond the optical limit presents advantages in
various applications. Considerable progress has been made in the design of meta …
various applications. Considerable progress has been made in the design of meta …
NeuralBO: A black-box optimization algorithm using deep neural networks
Bayesian Optimization (BO) is an effective approach for the global optimization of black-box
functions when function evaluations are expensive. Most prior works use Gaussian …
functions when function evaluations are expensive. Most prior works use Gaussian …
Automated tariff design for energy supply–demand matching based on Bayesian optimization: Technical framework and policy implications
HS Lee - Energy Policy, 2024 - Elsevier
With the emergence of renewable energy sources, designing tariffs becomes crucial to
match unstable energy supply with varying energy demand. However, traditional tariff …
match unstable energy supply with varying energy demand. However, traditional tariff …