Results 181 to 190 of about 106,055 (260)
ABSTRACT Hybrid modeling combines first‐principles equations with a data‐driven subcomponent. Training for the data‐driven part is sensitive to measurement noise when training targets are constructed using pointwise time derivatives. Beyond differentiation errors, hybrid models involve solving an inverse problem to estimate the data‐driven term, which ...
Hangjun Cho +4 more
wiley +1 more source
Development and Test of Highly Accurate End Point Free Energy Methods. 4. Expanding Solvents Capability and logBB Prediction. [PDF]
Niu T, He X, Man VH, Wang X, Wang J.
europepmc +1 more source
Abstract Transformer‐based molecular models pretrained on SMILES strings demonstrate strong performance in property prediction. However, these model often lack explicit integration of molecular surface charge distributions that govern intermolecular interactions such as hydrogen bonding and polarity.
Tae Hyun Kim +2 more
wiley +1 more source
The Use of Machine Learning to Estimate Ground Reaction Forces During Running: A Scoping Review of the Current Practices. [PDF]
Oliveira AS +2 more
europepmc +1 more source
A practical electrodialysis model for accelerating system development
Abstract Empirical optimization of electrodialysis (ED) is dependent on repetitive experiments with incremental adjustments, which is cost prohibitive at scale. While models can reduce the costs associated with optimization and scale‐up, existing ED models are limited in application to specific use cases and tend to be developed for the exploration of ...
Smith Pittman +3 more
wiley +1 more source
Protocol-aware epidemic forecasting across heterogeneous public health surveillance systems. [PDF]
Hu Y, Han J, Liu M.
europepmc +1 more source
Physics‐encoded transfer learning for scale‐up modeling of CHO cell bioreactors
Abstract Developing reliable predictive models for mammalian cell bioreactors, particularly Chinese hamster ovary (CHO) cultures widely used in biopharmaceutical manufacturing, remains challenging due to severe data scarcity in industrial‐scale reactors.
Muyang Li, Ming Xiao, Zhe Wu
wiley +1 more source
DC bias optimization in intelligent DCO-OFDM Li-Fi systems using hybrid machine learning with hardware validation. [PDF]
Abdelhakim E +3 more
europepmc +1 more source
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley +1 more source

