Search Results for: CAASE20

Real Digital Twin for a Complex Process

Data Driven models require Big-Data to perform well which is expensive, complicated to manage and in some cases difficult or impossible to get. However, PIMLᵀᴹ technology integrates physics and Machine Learning requiring only Small-Data.

Physics Informed Machine Learning (PIML™): Surrogate Models Providing Deeper Insights with Less Data

Patent Pending Physics Informed Machine Learning (PIML™) Surrogates: Have greater blind tested predictive accuracy than data-driven machine learning methods, Require significantly less data to train than data-driven machine learning methods, Provide greater insight into the underlying dynamics of the system than data-driven machine learning methods, Have been validated for a variety of complex problems

Simulation, AI & Digital Twins: An Intelligent Response to the COVID19 Pandemic

Digital twins most important mission may be to keep the human species alive long enough at the start of a pandemic virus that is highly lethal, very transmittable, and with long periods of asymptomatic transmission.

The Case for Democratizing Simulation

In this presentation from CAASE20, EASA's Sebastian Dewhurst provides an update on the state of democratizing simulation.

CAASE20: Additively Manufactured High-Performance Heat Exchangers

Maiki Vlahinos, Senior Application Engineer at nTopology, describes the simulation-driven methodology he followed to improve the performance of an advanced heat exchanger by 300%; specifically, a Fuel Cooled Oil Cooler for aerospace applications.