Results 71 to 80 of about 185 (177)
Optimization of the Production of Rubber Compounds Using Mathematical Models
Rubber compounds were mixed in a batch internal mixer, and symbolic regression was used to derive mathematical models linking recipe and process parameters to ram path, torque, and mixing quality (incorporation, dispersion, distribution). Subsequent optimization with evolutionary algorithms identified operating conditions that reduce specific energy ...
Anke Bardehle +7 more
wiley +1 more source
Fast Reflected Forward-Backward algorithm: achieving fast convergence rates for convex optimization with linear cone constraints. [PDF]
Boţ RI, Nguyen DK, Zong C.
europepmc +1 more source
Extremal index blocks estimator: the threshold and the block size choice. [PDF]
Gomes DP, Neves MM.
europepmc +1 more source
Classification logit two-sample testing by neural networks for differentiating near manifold densities. [PDF]
Cheng X, Cloninger A.
europepmc +1 more source
A novel workflow for investigating hydride vapor phase epitaxy for GaN bulk crystal growth is proposed. It combines Design of experiments (DoE) with physical simulations of mass transport and crystal growth kinetics, serving as an intermediate step between DoE and experiments.
J. Tomkovič +7 more
wiley +1 more source
The Small-N Series in the Zero-Dimensional O(N) Model: Constructive Expansions and Transseries. [PDF]
Benedetti D +3 more
europepmc +1 more source
Reproduction of stacking fault energy calculations from literature with a semi‐automated large language model‐assisted extraction procedure: extraction of simulation protocol, atomistic structures, computational parameters, and reported results, ontology alignment, knowledge graph construction and, finally, recomputation forvalidation.
Sepideh Baghaee Ravari +5 more
wiley +1 more source
Asymptotic Regularity of a Generalised Stochastic Halpern Scheme. [PDF]
Pischke N, Powell T.
europepmc +1 more source
Genuine modified Bernstein-Durrmeyer operators. [PDF]
Mohiuddine SA, Acar T, Alghamdi MA.
europepmc +1 more source
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer +4 more
wiley +1 more source

