Event · May 12, 2026

Discover STOCHOS Flow: free online seminar

PI Probaligence invites engineers and chemists to a free two-part webinar series on STOCHOS Flow on 23 and 24 June 2026, exploring the platform's capabilities for data-driven modeling, optimization, and decision support across chemistry and engineering applications.

Grid of the data types STOCHOS Flow handles, including scalar and tabular data, signals and time series, 2D and 3D fields, geometries and point clouds, images, graph and mesh data, molecular and formulation data, and multi-fidelity data, with the title Discover Stochos Flow

Webinar 1: focus on chemistry applications

This session provides an introduction to the capabilities of STOCHOS Flow and demonstrates practical applications in the chemical industry. Topics include formulation development, process optimization, and the analysis of cause-and-effect relationships to better understand and improve complex systems and product performance.

  • Date: June 23, 2026
  • Time: 10:00 to 11:00 CET
  • Duration: 1 hour
  • Language: English
  • Platform: Microsoft Teams

Register here

Webinar 2: focus on engineering applications

This webinar introduces the capabilities of STOCHOS Flow for engineering workflows and technical applications. Topics include the replacement of computationally expensive simulation models, optimization of geometries and engineering processes, and multi-fidelity approaches that combine experimental and simulation data for improved predictive performance and efficiency.

  • Date: June 24, 2026
  • Time: 10:00 to 11:00 CET
  • Duration: 1 hour
  • Language: English
  • Platform: Microsoft Teams

Register here

Speakers for both sessions: Dr.-Ing. Kevin Cremanns (CTO) and Jason Easaw (KAM).

Portrait of Dr.-Ing. Kevin Cremanns, CTO of PI Probaligence, labeled as seminar speaker
Speaker: Dr.-Ing. Kevin Cremanns, CTO
Portrait of Jason Easaw, Key Account Manager at PI Probaligence, labeled as seminar speaker
Speaker: Jason Easaw, KAM
Laptop showing the STOCHOS Flow node editor with a Bayesian optimization workflow on the canvas, a Python console, and the agent chat panel on the right
Next step
Product
STOCHOS Flow

Build, train, and deploy the workflow visually.

Guide
Build an ML workflow without an ML team

The engineer who owns the problem builds the model.

Interactive demo
Try to beat STOCHOS

Three use cases, about two minutes each.

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