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UID:pretalx-amfc2026-YFTZKP@modelica.simtek.cc
DTSTART;TZID=CST:20260920T151500
DTEND;TZID=CST:20260920T180000
DESCRIPTION:Agentic workflows developed for software development have chang
 ed our software development methods. Most of the trivial coding tasks are 
 solved by AI in a fraction of time of what the software engineer used to t
 ake. Instead of coding\, the focus of a software engineer is now on specif
 ying the work to be done\, choosing the right architecture\, orchestrating
  the agents doing the work\, and verifying the results.\nModelica – from
  its earlier definition – has always included a duality between the lang
 uage and the user interface artifacts. And the language is no different fo
 r an LLM than a programming language like C\, C++\, Python\, Julia\, Rust\
 , Wolfram\, etc. Therefore\, it took only a few months before a similar ch
 ange happened for modeling and simulation as for software engineering. We 
 do not model the same way as we did a year ago.\nIn this tutorial\, we wil
 l first discuss the recent and upcoming evolution of the role of the simul
 ation engineer towards a rather System Engineer type of work\, assisted by
  agents. The skills of today’s simulation engineer however remain critic
 al for successful simulations. We will demonstrate this by hands-on exampl
 es. In a second step\, we will leverage Wolfram System Modeler and their n
 ewest agent skills to develop a detailed Modelica in an agentic manner\, p
 ointing at best practices and pitfalls. \nAt the end of this workshop\, yo
 u will be equipped to develop your own model in a much faster and efficien
 t way\, as AI-embracing simulation engineer.
DTSTAMP:20261004T070804Z
LOCATION:Modelica Technology & AI (R1001)
SUMMARY:Workshop 4: Hands-on agentic modeling and best practices\, with Wol
 fram System Modeler - Clément Coïc
URL:https://modelica.simtek.cc/amfc2026/talk/YFTZKP/
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BEGIN:VEVENT
UID:pretalx-amfc2026-YFSEAH@modelica.simtek.cc
DTSTART;TZID=CST:20260921T134900
DTEND;TZID=CST:20260921T135000
DESCRIPTION:This session is chaired by:
DTSTAMP:20261004T070804Z
LOCATION:Modelica Technology & AI (R1001)
SUMMARY:T1S2-Session Chair - Clément Coïc
URL:https://modelica.simtek.cc/amfc2026/talk/YFSEAH/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-amfc2026-NVZJRC@modelica.simtek.cc
DTSTART;TZID=CST:20260921T153400
DTEND;TZID=CST:20260921T153500
DESCRIPTION:This session is chaired by:
DTSTAMP:20261004T070804Z
LOCATION:Modelica Technology & AI (R1001)
SUMMARY:T1S3-Session Chair - Clément Coïc
URL:https://modelica.simtek.cc/amfc2026/talk/NVZJRC/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-amfc2026-J78WAX@modelica.simtek.cc
DTSTART;TZID=CST:20260921T160000
DTEND;TZID=CST:20260921T162500
DESCRIPTION:In this work\, we compare a traditional design-ofexperiments (D
 oE) workflow\, defined up-front and then run\, against an agentic AI workf
 low that revises its plan as the data arrives. The use case is a chassis o
 ptimization on the standard compact example in the Modelon VehicleDynamics
  Library. Both workflows produce a recommendation. The DoE arrives at a cl
 ean Pareto front and a stiffness change. The agentic path is longer: along
  the way it flags a controller saturation in the DoE winner\, traces a cen
 ter-ofgravity asymmetry to a load sensitivity the original scope did not c
 over\, and finds a kinematic ceiling outside the four DoE parameters. It e
 nds up changing a different parameter\, and that parameter turns out to do
  more than the static KPIs asked for: dynamic verification shows the bushi
 ng change roughly halves the fishhook return lag unloaded. The architectur
 e that makes this possible combines validated physics\, Modelon Impact\, t
 he Model Context Protocol that connects them to an LLM\, and a knowledge b
 ase layered by lifetime. We close with what we learned about when each app
 roach is the right tool.
DTSTAMP:20261004T070804Z
LOCATION:Modelica Technology & AI (R1001)
SUMMARY:From Intent to Action: Agentic AI on Validated Modelica Libraries -
  Johan Andreasson\, Ajith Kumar\, Clément Coïc
URL:https://modelica.simtek.cc/amfc2026/talk/J78WAX/
END:VEVENT
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UID:pretalx-amfc2026-XB79KT@modelica.simtek.cc
DTSTART;TZID=CST:20260922T115500
DTEND;TZID=CST:20260922T122000
DESCRIPTION:This paper introduces State-Invariant Hybrid Models (SIHM)\, a 
 subset of hybrid physics-machine-learning models in which the state vector
  remains defined by the first-principles Modelica model\, while learned co
 mponents contribute missing physics locally. We qualitatively contrast glo
 bal residual learning at system level with local residual learning at comp
 onent level\, arguing that local learning better matches the acausal model
 ing paradigm of Modelica. A tool-agnostic workflow is proposed in which a 
 replaceable dummy component marks the suspected location of missing physic
 s\, while compiler-derived structural information identifies the connected
  states and variables. The workflow is then illustrated in the Wolfram eco
 system on two models to demonstrate the approach's modularity. The results
  indicate that local SIHM components recover targeted missing effects whil
 e preserving the state selection of the original model.
DTSTAMP:20261004T070804Z
LOCATION:Modelica Technology & AI (R1001)
SUMMARY:Learning the Missing Physics Locally with Modelica and Wolfram Lang
 uage - Clément Coïc\, Ankit Naik
URL:https://modelica.simtek.cc/amfc2026/talk/XB79KT/
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