Nico Formanek

Department head

Project Reproducibility and Simulation avoidance

Science, especially scientific computer simulations aim to be reproducible. HPC simulations as for example done here at HLRS often run into reproducibility issues, caused not only by infrastructural issues such as dark data or backward compatibility. Because of parallelization it can be the case that even bit-wise reproducibility is not achievable. This poses somewhat of a dilemma for simulation science. There is an expectation of strong reproducibility which can in practice only partially be fulfilled. One goal of this project is to shed light on the notion of reproducibility in simulation science, comparing it to similar notions from neighboring fields such as computational statistics. How should reproducibility expectations be balanced with what simulations can realistically deliver? Another question which I try to answer is what kind of reproducibility we should expect from hybrid and surrogate simulations. Currently ML methods, because of their cheaper computational costs are introduced in the simulation pipeline everywhere. They come with their own reproducibility issues, stemming from different sources than those in computer simulations. I try to figure out how these issues interfere and what kind of properties computational problems must have to be amenable to the “ML-treatment”.

Blog

You can find scattered thoughts mostly on the philosophy of computational methods on the workshopping blog I run with Ramón Alvarado.

Scientific interests
  • Philosophy of science/philosophy of technology
    • Intertheoretical relations in physical theories (reduction, derivation, founding, modularity etc.)
    • Computer simulatable physical theories (e.g. LatticeQCD)
    • Computational reliabilism
    • Constructing artifacts as pragmatic justification of scientific theories
    • discrete vs continuous science
  • Limits of statistical inference/learning
    • Inductive assumptions in approxmative methods
    • Machine Learning & the problem of induction
    • Kolmogorov complexity as general framework for ML
    • theoretical vs. practical limits (e.g. no free lunch theorems vs. FLOPs)
  • Science and society
    • Why trust science?
    • Why trust society?
Work In Progress

Working for trust – when easy things become hard (Abstract)

The theory and practice of computational error (Abstract – of a talk with the same title given at IACAP 23)

Recent and forthcoming publications

Publications that I especially like:

Upcoming Talks
Recent talks

Slides in links.

Teaching (Current Semester)
Simulierte Welten

Current Project:

I am currently supervising three high-school students in the project Simulierte Welten. We are implementing a simplified version of Langley’s BACON program for automated scientific inference on the arduino platform. The goal is to use the on board sensors of the arduino nano sense to automatically discover simple physical laws.

Past Project:

In 23/24 I supervised two high-school students in the project Simulierte Welten. We developed a topical model for the PhilosL mailinglist. This model automatically extract topics (i.e. metaphysics, philosophy of science, ethics) from the archives of the mailing list, allows to observe trends in topical distribution and evolution – and correlate topics with other interesting quantities. A more detailed description of the project can be found here (in German).