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We harness the power of machine learning and data analysis to real challenges in the copper industry. Our documentation covers data preprocessing, feature engineering, classification, regression, and model selection. Discover how we've optimized predictive capabilities for manufacturing solutions.
A Python toolkit/library for reality-centric machine/deep learning and data mining on partially-observed time series, including SOTA neural network models for scientific analysis tasks of imputation, classification, clustering, forecasting, & anomaly detection on incomplete industrial (irregularly-sampled) multivariate TS with NaN missing values
Siemens Industrial Experience is a design system for designers and developers, to consistently create the perfect digital experience for industrial software products.
AMO-Tools-Suite is an energy efficiency calculation library in C++ with optional Nan Node add-on bindings for the Department of Energy Advanced Manufacturing Office (DOE AMO) Desktop, also known as MEASUR.
This Guidance demonstrates how to create an intelligent manufacturing digital thread through a combination of knowledge graph and generative artificial intelligence (AI) technologies. A digital thread offers an integrated approach to combine disparate data sources across enterprise systems, increasing traceability, accessibility, collaboration.
AMO-Tools-Desktop is an energy efficiency calculation application for use with industrial equipment such as pumps, furnaces, fans, and motors, as well as for industrial systems such as steam. Currently in beta.