Results 81 to 90 of about 110,828 (307)

Data driven analysis of tablet design via machine learning for evaluation of impact of formulations properties on the disintegration time

Ain Shams Engineering Journal
This study investigated the use of advanced machine learning techniques to model disintegration time for solid dosage oral formulations. The input features encompass molecular properties, physical attributes, excipient compositions, and formulation ...
Mohammed Ghazwani, Umme Hani
doaj   +1 more source

Inverse Identification of Energy‐Dependent Laser Absorptivity in NiTi Laser Powder‐Bed Fusion via Calibrated Melt Pool Simulation

Advanced Engineering Materials, EarlyView.
A combined experimental–computational framework identifies energy‐dependent laser absorptivity for NiTi in laser powder‐bed fusion, applicable to conduction and transition modes. Single‐track experiments and thermofluid smoothed particle hydrodynamics simulations are coupled through inverse analysis of melt pool geometry.
Mohamadreza Afrasiabi, Hadi Moutablaleh, Ignatius A. Setiawan, Mehrshad Mehrpouya   +3 more
wiley   +1 more source

Analytic Moment-based Gaussian Process Filtering [PDF]

, 2009
04.07.13 KB. Ok to add accepted version to Spiral, authors retain copyright.We propose an analytic moment-based filter for nonlinear stochastic dynamic systems modeled by Gaussian processes.
Huber, MF, Deisenroth, MP, Hanebeck, UD
core  

Crowdsourcing Spatial Phenomena Using Trust-Based Heteroskedastic Gaussian Processes

, 2013
Many crowdsourcing applications require spatial modelling of data to make sense of location-based observations provided by multiple users. In this context, We propose a new spatial function modelling approach to address the problem of fusing multiple ...
Jennings, N. R., Alex Rogers, Nicholas R. Jennings, Rogers, Alex, Matteo Venanzi, Venanzi, Matteo, Jennings, Nicholas   +6 more
core   +1 more source

Constructing coarse-grained models with physics-guided Gaussian process regression

APL Machine Learning
Coarse-grained models describe the macroscopic mean response of a process at large scales, which derives from stochastic processes at small scales. Common examples include accounting for velocity fluctuations in a turbulent fluid flow model and cloud ...
Yating Fang, Qian Qian Zhao, Ryan B. Sills, Ahmed Aziz Ezzat   +3 more
doaj   +1 more source

Counterion Dependent Side‐Chain Relaxation Stiffens a Chemically Doped Thienothiophene Copolymer

Advanced Functional Materials, EarlyView.
Oxidation of a thienothiophene copolymer, p(g3TT‐T2), via different doping strategies and dopant molecules resulted in materials with similar oxidation levels and a high electrical conductivity of ≈100 S cm−1. However, mechanical properties varied significantly, with sub‐glass transition temperatures and elastic moduli spanning from –44°C to –3°C and ...
Mariavittoria Craighero, Meghna Jha, Elsa Veronica Flores‐Vela, Joost Kimpel, Andreas Schaefer, Jiali Guo, Jesika Asatryan, Alberto Peinador Veiga, Shuichi Haraguchi, Per‐Anders Carlsson, Jaime Martín, Mariano Campoy‐Quiles, Christian Müller   +12 more
wiley   +1 more source

The pitfalls of using Gaussian Process Regression for normative modeling.

PLoS ONE, 2021
Normative modeling, a group of methods used to quantify an individual's deviation from some expected trajectory relative to observed variability around that trajectory, has been used to characterize subject heterogeneity.
Bohan Xu, Rayus Kuplicki, Sandip Sen, Martin P Paulus   +3 more
doaj   +1 more source

Machine Learning‐Assisted Inverse Design of Soft and Multifunctional Hybrid Liquid Metal Composites

Advanced Functional Materials, EarlyView.
A machine learning framework is presented for inverse design of synthesizable multifunctional composites containing both liquid metal and solid inclusions. By integrating physics‐based modeling, data‐driven prediction, and Bayesian optimization, the approach enables intelligent design of experiments to identify optimal compositions and realize these ...
Lijun Zhou, Yunsik Ohm, Ren‐Mian Chin, Olivia Kerr, Krithika Manohar, Mohammad H. Malakooti   +5 more
wiley   +1 more source

Gaussian Process priors with uncertain inputs? Application to multiple-step ahead time series forecasting [PDF]

, 2003
We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model. k-step ahead forecasting of a discrete-time non-linear dynamic system can be performed by doing repeated one-step ahead ...
Quiñonero Candela, J, Rasmussen, C., ,, Murray-Smith, Roderick, Rasmussen, C.E., Quinonero-Candela, J., Rasmussen, Carl Edward, Quiñonero-Candela, Joaquin, Girard, A., Rasmussen, CE, Murray-Smith, R., Girard, A, Girard, Agathe, Murray Smith, R   +13 more
core  

Prediction of soil hydraulic properties by Gaussian process regression algorithm in arid and semiarid zones in Iran

, 2021
The soil water retention curve (SWRC) is one of the principal soil hydraulic properties that is needed as input data in modeling water and solute transport through unsaturated soils.
Bayat, H., Mansoorizadeh, M., Gregory, A. S., Rastgou, M.   +3 more
core   +1 more source