Dr. Jan Friedrich
  • Home
  • Publications
  • Conferences
  • Teaching
  • Short CV
  • Publications
    • Control of Conservation Laws in the Nonlocal-to-Local Limit
    • Source identification in bioluminescence tomography by consensus-based optimization
    • Boundary Stabilization with restricted observability
    • Central schemes for systems of non-local balance laws
    • A non-local traffic flow model for 1-to-1 junctions with buffer
    • Conservation Laws with Nonlocal Velocity: The Singular Limit Problem
    • Conservation Laws with Nonlocality in Density and Velocity and Their Applicability in Traffic Flow Modelling
    • Conservation laws with discontinuous flux function on networks: a splitting algorithm
    • Lyapunov Stabilization for Nonlocal Traffic Flow Models
    • Lyapunov stabilization of a nonlocal LWR traffic flow model
    • Numerical schemes for a class of nonlocal conservation laws: a general approach
    • Modeling Multilane Traffic with Moving Obstacles by Nonlocal Balance Laws
    • Network models for nonlocal traffic flow
    • Nonlocal approaches for multilane traffic models
    • Traffic flow models with nonlocal velocity
    • A non-local traffic flow model for 1-to-1 junctions
    • Micro-macro limit of a nonlocal generalized Aw-Rascle type model
    • Maximum principle satisfying CWENO schemes for nonlocal conservation laws
    • A Godunov type scheme for a class of LWR traffic flow models with non-local flux
  • Short CV
  • Conferences
    • Conservation laws with space-dependent nonlocalities
    • Maximum principle satisfying CWENO schemes for multidimensional nonlocal conservation laws
    • Numerical schemes for conservation laws with space-dependent nonlocalities
    • Balance laws with space-dependent nonlocalities - modeling, simulation, UQs
    • Numerical schemes for conservation laws with space-dependent nonlocalities
    • Nonlocal balance laws - Numerical schemes and applications to traffic flow
    • Source identification via a consensus-based optimization algorithm using different moment hierarchies
    • Numerical schemes for conservation laws with space-dependent nonlocalities
    • Lyapunov Stabilization for Nonlocal Traffic Flow Models
    • Source identification using different moment hierarchies and consensus-based optimization
    • Cell tracking for the radiative transfer equation
    • Traffic flow models with nonlocal velocity - The singular limit problem
    • Cell tracking for the radiative transfer equation
    • Cell tracking using uncertainty quantification for the radiative transfer equation
    • Aspects of nonlocal traffic flow modeling
    • Lyapunov stabilization for nonlocal traffic flow models
    • Lyapunov stabilization of nonlocal traffic flow models
    • Traffic flow models with nonlocal velocity - The singular limit problem
    • Lyapunov stabilization of nonlocal traffic flow models
    • Nonlocal approaches for multilane traffic models
    • Network models for nonlocal traffic flow
    • Maximum principle satisfying CWENO schemes for non-local conservation laws
    • Traffic flow models with non-local flux and approaches for network models
    • Network Models and Numerical Methods for Traffic Flow with Non-Local Flux Terms
    • A Godunov type scheme for a class of scalar conservation laws with non-local flux
  • Projects
  • Projects
    • Pandas
    • PyTorch
    • scikit-learn
  • Teaching
    • Learn JavaScript
    • Learn Python

scikit-learn

Oct 2023 · 1 min read
Go to Project Site

scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.

Last updated on Oct 2023
Hugo Wowchemy Markdown
Jan Friedrich
Authors
Jan Friedrich
Research Associate / Lecturer (Akademischer Rat)
← PyTorch Oct 2023

© 2025 Jan Friedrich. Room 03.06.055. Technical University of Munich. School of Computation, Information and Technology Department of Mathematics. Boltzmannstraße 3, 85748 Garching b. Munich, Germany.

Published with Hugo Blox Builder — the free, open source website builder that empowers creators.