Results 131 to 140 of about 5,021 (265)

Critical evaluation of the theory and practice of feed-forward neural networks for genomic prediction. [PDF]

G3 (Bethesda)
Kusmec A, Negus KL, Yu J.
europepmc   +1 more source

Trust‐region filter algorithms utilizing Hessian information for gray‐box optimization

AIChE Journal, EarlyView.
Abstract Optimizing industrial processes often involves gray‐box models that couple algebraic glass‐box equations with black‐box components lacking analytic derivatives. Such systems challenge derivative‐based solvers. The classical trust‐region filter (TRF) algorithm provides a robust framework but requires extensive parameter tuning and numerous ...
Gul Hameed, Tao Chen, Antonio del Rio Chanona, Lorenz T. Biegler, Michael Short   +4 more
wiley   +1 more source

Optimizing genomic selection models for wheat breeding under contrasting water regimes in a mediterranean environment. [PDF]

Plant Methods
Yannam VRR, Lopes MS, Soriano JM.
europepmc   +1 more source

Sequential Monte Carlo with likelihood tempering and parallel implementation for uncertainty quantification

AIChE Journal, EarlyView.
Abstract Bayesian estimation enables uncertainty quantification, but analytical implementation is often intractable. As an approximate approach, the Markov Chain Monte Carlo (MCMC) method is widely used, though it entails a high computational cost due to frequent evaluations of the likelihood function.
Tatsuki Maruchi, Tomoyuki Yajima, Yoshiaki Kawajiri   +2 more
wiley   +1 more source

Introducing the kernel descent optimizer for variational quantum algorithms. [PDF]

Sci Rep
Simon L, Eble H, Radons M.
europepmc   +1 more source

On reproducing kernel methods in functional statistics

, 2018
Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Matématicas.
openaire   +1 more source

Feature Selection for Machine Learning‐Driven Accelerated Discovery and Optimization in Emerging Photovoltaics: A Review

Advanced Intelligent Discovery, EarlyView.
Feature selection combined with machine learning and high‐throughput experimentation enables efficient handling of high‐dimensional datasets in emerging photovoltaics. This approach accelerates material discovery, improves process optimization, and strengthens stability prediction, while overcoming challenges in data quality and model scalability to ...
Jiyun Zhang, Jiayi Tan, Qizhen Song, Tian Du, Jens Hauch, Christoph J. Brabec   +5 more
wiley   +1 more source

Harnessing genomic prediction in Brassica napus through a nested association mapping population. [PDF]

Plant Genome
Perumal S, Higgins EE, Sra S, Khedikar Y, Moore J, Chaudary R, Haile T, Koh CS, Vail S, Robinson SJ, Horner K, Hope B, Klein-Gebbinck H, Herrmann DT, Navabi K, Sharpe AG, Parkin IAP.   +16 more
europepmc   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Using a meshless method to investigate the effects of confining pressure on the hydraulic fracturing processes of hydraulic tunnels. [PDF]

Sci Rep
Zhang H, Shi Y, Mu J, Hao R, Qi D, Li W, Zhang B, Yu S.   +7 more
europepmc   +1 more source