Simultaneous Parameter Learning and Bi-clustering for Multi-Response Models. [PDF]
Front Big Data, 2019 Yu M +3 more
europepmc +1 more source
Advanced Engineering Materials, EarlyView.We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang +2 more
wiley +1 more source
Optimal structure and parameter learning of Ising models. [PDF]
Sci Adv, 2018 Lokhov AY +3 more
europepmc +1 more source
Machine learning for parameter estimation
Proceedings of the National Academy of Sciences, 2023 openaire +2 more sources
Microstructure Evolution of a VMnFeCoNi High‐Entropy Alloy After Synthesis, Swaging, and Annealing
Advanced Engineering Materials, EarlyView.The synthesis and processing (rotary swaging and annealing) of the novel VMnFeCoNi alloy is investigated, alongside the estimation of the grain size effect on hardness. Analysis of a wide grain size range of recrystallized microstructures (12–210 µm) reveals a low annealing twin density.
Aditya Srinivasan Tirunilai +6 more
wiley +1 more source
word2vec Parameter Learning Explained
, 2016 The word2vec model and application by Mikolov et al. have attracted a great amount of attention in recent two years. The vector representations of words learned by word2vec models have been proven to be able to carry semantic meanings and are useful in ...
Xin Rong
core
Vehicle Deceleration Prediction Model to Reflect Individual Driver Characteristics by Online Parameter Learning for Autonomous Regenerative Braking of Electric Vehicles. [PDF]
Sensors (Basel), 2019 Min K, Sim G, Ahn S, Sunwoo M, Jo K.
europepmc +1 more source
A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science
Advanced Engineering Materials, EarlyView.We unveil a prototype hybrid‐workflow framework that fuses automatedcomputation with hands‐on experiments. Built atop pyiron, a lightweight, parameterized layer translates procedure descriptions into executable manual steps, syncing instrument settings, human interventions, and data capture in real‐time today.
Steffen Brinckmann +8 more
wiley +1 more source
Structure Learning with Distributed Parameter Learning for Probabilistic Ontologies
, 2015 We consider the problem of learning both the structure and the parameters of Probabilistic Description Logics under DISPONTE. DISPONTE (“DIstribution Semantics for Probabilistic ONTologiEs”) adapts the distribution semantics for Probabilistic Logic ...
RIGUZZI, Fabrizio +4 more
core
When and Where to Transfer for Bayes Net Parameter Learning. [PDF]
Expert Syst Appl, 2016 Zhou Y, Hospedales TM, Fenton N.
europepmc +1 more source