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SUMMARY:Introduction to Simulation Based Inference: Enhancing Synthetic Mo
 dels with Artificial Intelligence
DTSTART:20260907T110000Z
DTEND:20260908T150000Z
DTSTAMP:20260901T052200Z
UID:indico-event-506@events.hpc-portal.eu
CONTACT:al.bazarova@fz-juelich.de
DESCRIPTION:This course will take place as an online event. The link to th
 e streaming platform will be provided to the accepted registrants only. Th
 e course will be held in English.\nCourse Content:\nThis tutorial introduc
 es Simulation-Based Inference (SBI)\, a framework combining Bayesian model
 ing\, AI techniques\, and high-performance computing (HPC) to address key 
 challenges\, such as performing reliable inference with limited data by us
 ing AI-based approximate Bayesian computation. Moreover\, it tackles the p
 roblem of intractable likelihood functions\, thereby allowing to utilize B
 ayesian inference for biological systems with multiple sources of stochast
 icity. The tutorial also demonstrates how to leverage HPC environments to 
 drastically reduce inference runtimes\, making it highly relevant for larg
 e-scale biological problems. This tutorial bridges theoretical foundations
  with hands-on applications realized via jupyter notebooks.\nLearning Obje
 ctives\n\nUnderstand the Principles of Simulation-Based Inference (SBI): l
 earn the theoretical foundations of SBI\, including its relationship with 
 Bayesian inference and its advantages in handling complex systems.\n\n\n\n
 Explore SBI Methods (SNPE\, SNLE\, and SNRE): gain an understanding of Seq
 uential Neural Posterior Estimation (SNPE)\, Sequential Neural Likelihood 
 Estimation (SNLE)\, and Sequential Neural Ratio Estimation (SNRE) and thei
 r applications.\n\n\n\n\nLearn how to design and implement SBI frameworks 
 for representative scenarios\, such as molecular dynamics\, cell growth\, 
 count data modeling\, and Lotka-Volterra systems.\n\n\n\n\nLeverage HPC fo
 r SBI Workflows: understand how to use high-performance computing (HPC) en
 vironments to scale SBI workflows and efficiently distribute computational
  workloads.\n\n\n \n\n\n\n\nContents level\n\n\nin hours\n\n\nin %\n\n\n\
 n\n\n\nBeginner's contents:\n\n\n0 h\n\n\n0 %\n\n\n\n\nIntermediate conten
 ts:\n\n\n4 h\n\n\n50 %\n\n\n\n\nAdvanced contents:\n\n\n4 h\n\n\n50 %\n\n\
 n\n\nCommunity-targeted contents:\n\n\n0 h\n\n\n0 %\n\n\n\n\n \nPrerequis
 ites:\nAlthough the course is giving a brief introduction into Bayesian st
 atistics and AI methods involved in building an SBI framework\, we also ex
 pect basic familiarity with statistical and deep learning concepts. Experi
 ence of working with HPC systems would be beneficial but is not strictly r
 equired.\nA personal institutional email address (university/research inst
 itution\, government agency\, organisation\, or company) is required to re
 gister for JSC training courses. If you don't have an institutional email 
 address\, please get in touch with the contact person for this course.\nTa
 rget Audience:\nScientists who are willing to speed up their Bayesian infe
 rence methods using AI-based tools and simulations. Scientists who are wil
 ling to take their Bayesian inference to the next level by handling intrac
 table likelihoods. Scientists who are willing to enhance their simulations
  with AI-based inference methods for uncertainty quantification.\nLearning
  Outcome:\nThe ability to set up a Bayesian approach within a given framew
 ork\nLanguage:\nThis course is given in English.\nDuration:\n2 half days\n
 Dates:\n7-8 September 2026\, 13:00-17:00 each day\nVenue:\nOnline\nNumber 
 of Participants:\nMaximum 25\nInstructors:\nAlina Bazarova\, Jose Robledo\
 n\nhttps://events.hpc-portal.eu/event/506/
LOCATION:Online
URL:https://events.hpc-portal.eu/event/506/
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