Machine-learned multi-system surrogate models for materials prediction
نویسندگان
چکیده
منابع مشابه
Surrogate models for aerodynamic performance prediction
Automatic optimisers can play a vital role in the design and development of engineering systems and processes. However, a lack of available data to guide the search can result in the global optimum solution never being found. Surrogate models can be used to address this lack of data and allow more of the design space to be explored, as well as provide an overall computational saving. In this th...
متن کاملImproving Multi-Step Prediction of Learned Time Series Models
Most typical statistical and machine learning approaches to time series modeling optimize a singlestep prediction error. In multiple-step simulation, the learned model is iteratively applied, feeding through the previous output as its new input. Any such predictor however, inevitably introduces errors, and these compounding errors change the input distribution for future prediction steps, break...
متن کاملInsights from Machine-Learned Diet Success Prediction
To support people trying to lose weight and stay healthy, more and more fitness apps have sprung up including the ability to track both calories intake and expenditure. Users of such apps are part of a wider "quantified self" movement and many opt-in to publicly share their logged data. In this paper, we use public food diaries of more than 4,000 long-term active MyFitnessPal users to study the...
متن کاملPrediction using Machine Learned Constraint Satisfaction Programs
Prediction is a well-researched area for Machine Learning applications. In these tasks, the aim is to predict the value for some unseen characteristic based upon the values of other, seen, characteristics for a given example. Machine learning has been extensively applied to these types of tasks by automating the derivation of a predictive function or a set of predictive rules. This predictive f...
متن کاملSubjective Ordinal Labels: Implications for Hypothesis Testing and Machine Learned Prediction Models
In practice, due to the cost of labeling or tagging, subjective data is often used without adjustments for label uncertainty. This problem falls into the gap between textbook applications of machine learning techniques and research level methodology. A variety of approaches have been discussed in detail throughout the statistical research literature but more effort could be made to motivate thi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: npj Computational Materials
سال: 2019
ISSN: 2057-3960
DOI: 10.1038/s41524-019-0189-9