a new era in machine learning

Engineering

PI probaligence gmbh

Computer Aided Egineering

STOCHOS used in CAE

Machine Learning has become an important tool in the realm of computer simulation, particularly in applications involving Finite Element Method (FEM). ML techniques are employed to enhance the efficiency, accuracy, and optimization of simulations across various domains.
The integration of STOCHOS in computer simulation marks a significant leap forward in predictive modeling and analysis. This synergy empowers engineers and researchers to tackle increasingly complex problems, optimize designs, and explore innovative solutions in a more efficient and data-driven manner across various industries.

STOCHOS

Adaptive model improvement

New samples are suggested according to the uncertainty of the model

STOCHOS models can learn continuously as new data becomes available. Rather than training the model once and keeping it static, it is updated regularly to capture evolving patterns and trends.

The adaptive model improvement is especially valuable in real-world applications where the environment is dynamic, and the data landscape evolves over time. It allows ML models to remain effective and relevant in scenarios where static models might become outdated or less accurate.

Probabilistic Intelligence in Simulation

The Impact of STOCHOS on Computer-Aided Engineering (CAE)

In the dynamic field of engineering, the infusion of Machine Learning into Computer-Aided Engineering is reshaping traditional approaches to problem-solving. CAE, known for simulating complex physical systems, has found a powerful ally in ML, bringing adaptability and efficiency to the forefront.

By learning from extensive datasets, our algorithms discern intricate patterns and relationships, particularly beneficial in scenarios where traditional models struggle with nonlinearity or numerous variables.

Reducing computational burdens is another advantage of STOCHOS by approximating complex simulations, accelerating design iterations and enabling real-time decision-making in dynamic scenarios.

From optimizing designs to predicting structural integrity, the synergy between ML and CAE empowers engineers to push boundaries and innovate in previously impractical ways.

Partnership with ANSYS

Since 2022 our AI/ML algorithms are used by Ansys OptiSLang

To Ansys

Part of CADFEM Group

Since Nov 2023 we are a part of the CADFEM Group

To CADFEM Group

Joined Project Partner High Fidelity Digital Twins using AI

Since Oct 2022 Probaligence participates in a research project targeting the simulation of complex composite structures.

To the project

A New Era in Machine Learning

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