Abstract: The present disclosure related to a silicone-based foam may include a component A and a component B, where the component A may include a silicone-based matrix component, a first filler component that may include alumina trihydrate, a second filler component that may include perlite, and a third filler component that may include calcium carbonate, and where the component B may include a silicone-based matrix component, a first filler component that may include alumina trihydrate, a second filler component that may include perlite, a third filler component that may include calcium carbonate, and a fourth filler component that may include zinc borate. The silicone-based foam may have a V-0 flammability rating as measured according to ASTM D3801.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. A portfolio return computation request configured to specify simulated market scenarios generated using neural networks and a set of filters is obtained. Constituent portfolio securities of a portfolio are determined. The simulated market scenarios are filtered based on the set of filters. Expected returns for the constituent portfolio securities are retrieved.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. A portfolio return computation request configured to specify simulated market scenarios generated using multi-variate mixture datastructures and a set of filters is obtained. Constituent portfolio securities of a portfolio are determined. The simulated market scenarios are filtered based on the set of filters. Expected returns for the constituent portfolio securities are retrieved.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. An asset return metrics calculation request datastructure is obtained. The number of sessions to utilize for calculating asset return metrics data is determined.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. A portfolio construction request configured to include a set of optimization parameters is obtained. A set of simulated market scenarios is generated using multi-variate mixture datastructures. A set of expected returns for securities in the universe of securities for the set of simulated market scenarios is retrieved.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. User selection of simulated market scenarios generated using multi-variate mixture datastructures is obtained. A range of unfiltered simulated market factor values for each market factor is determined. Customized market factors are updated based on a user modification. A range of allowable values for each customized market factor is determined.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. User selection of simulated market scenarios generated using neural networks is obtained. A range of unfiltered simulated market factor values for each market factor is determined. Customized market factors are updated based on a user modification. A range of allowable values for each customized market factor is determined.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. Neural networks are used as encoder to generate a set of latent variables. Latent variables are simulated with neural networks as decoder such that the decoded simulated market scenarios follow dynamic dependencies and volatilities of historical market risk factors.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. A portfolio construction request configured to include a set of optimization parameters is obtained. A set of simulated market scenarios is generated using neural networks. A set of expected returns for securities in the universe of securities for the set of simulated market scenarios is retrieved.