A New ERA in Machine Learning

STOCHOS©

PI Probaligence gmbh

PI
DIM-GP
STOCHOS

STOCHOS Software Package

The hidden champion in ML-guided Engineering and Chemistry

  • Reliable insights from less data
    Generate accurate predictions without the need for large datasets.

  • Continuously improving performance
    Models automatically adapt and refine as new information becomes available.

  • Effortless to use
    No expert knowledge, parameter tuning, or domain customization required.

  • Fast and cost-efficient
    Runs smoothly on standard hardware—no expensive cloud infrastructure needed.

  • Built-in noise resilience
    Automatically identifies and manages experimental noise for robust results.

Probabilistic

Using Gaussian processes (GP),
STOCHOS pinpoints the most likely function for accurate modeling and predictions.                                                                                      

Effective

To determine the next training point, STOCHOS only looks on regions with elevated uncertainties to maximize the information gain and find the global optima.

Standard neural networks offer scalability, batch learning, and excel in state-of-the-art solutions for tasks like picture classification or time series analysis. On the other hand, Gaussian Processes require only a minimal amount of input data, involve little to no hyperparameters, allow for model uncertainty calculation, and are proficient at noise detection.

STOCHOS redefines the boundaries of what is achievable. Whether tackling complex classification challenges or navigating intricate time series analyses, this algorithm’s unique fusion of ANNs and Gaussian processes sets new standards in the realm of machine learning.

Utilizing STOCHOS proprietary algorithms, you can excel in efficiently identifying correlations between input and output parameters. The STOCHOS set of algorithms is specifically designed to navigate through complex datasets, ensuring to uncover meaningful relationships with precision and effectiveness.

Accelerate

  • Accelerate the R&D processes
  • Save resources and reduce costs
  • Speed up time to market

Improve

  • Maximize the performance with minimal resources
  • Dynamically explore design spaces

Understand

  • Identify key performance drivers
  • Improve transparency
  • Make confident, data-driven decisions

Scale

  • Share AI tools with non-technical teams & customers
  • Integrate models seamlessly into workflows

DIM-GP & GEN-AI

Bayesian Opt.

Sensivity Analysis

WebApp Export

Multi-fidelity Modeling

Multi-Fidelity-Modeling combines data of different accuracy and cost to build better predictive models. High-fidelity data, such as precise experiments or detailed simulations, is very accurate but expensive, while low-fidelity data is faster and cheaper but less reliable. h STOCHOS generates high fidelity models using training data from different fidelity levels.

Design of Experiments

STOCHOS can significant improve your Design of Experiments (DOE) by enhancing the efficiency, effectiveness, and insight generation during the experimental process. 
By leveraging STOCHOS techniques in DOE, researchers can streamline the experimentation process, gain deeper insights into complex systems, and make more informed decisions with limited resources.
We have extensive experience, particularly in coatings and similar chemical applications.

Example ->

Global and local Sensivity Analysis

STOCHOS includes advanced sensitivity analysis tools, supporting both global and local perspectives. For global analysis, it computes Sobol indices that capture linear and non-linear, monotonic multivariate dependencies, providing a comprehensive understanding of the relative importance of inputs. Complementarily, the local sensitivity module evaluates how small perturbations in input parameters influence the system, predicting the overall model behaviour under localised variations. This dual capability enables users to assess both broad parameter relevance and fine-grained response dynamics within a unified probabilistic setting.

Automatice export functionality

WebApp STOCHOS

The Python-based framework designed for flexible modeling of scalars, signals, fields or meshes, allowing users to define and fit the STOCHOS generated models to a wide variety of data structures. By the built-in web application export models can be deployed directly as interactive dashboards, enabling live visualization, parameter tuning, and predictive exploration. 

Book a short remote meeting with us to learn more about STOCHOS and its possibilities.

Contact us

Address

  • Am Schammacher Feld 37
    85567 Grafing b. München, Germany
  • +46 160 45 090 24
  • info@probaligence.com

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