Skip to content

Synergies of Machine Learning and Numerics

About the workshop

We dive into the exciting intersection of machine learning and numerical methods, exploring how they can be combined to create powerful new tools. As machine learning transforms countless fields, its fusion with traditional numerical approaches can be a game-changer for inverse problems, partial differential equations, sampling, classification, etc. Leading experts and young researchers will share their insights and showcase successful collaborations between these two fields. During the workshop, we will:

  • foster collaborations via interdisciplinary discussions,
  • explore the exciting synergies between machine learning and numerics, and
  • discover innovative approaches to tackle complex computational challenges.

The International Conference on Scientific Computing and Machine Learning (SCML2025) will be held in nearby Kyoto the week before the workshop, offering a convenient opportunity to attend both events.

Participation

If you are interested in attending the presentations at our upcoming workshop, please register by emailing osaka25@rupp.ink. We look forward to your participation!

Confirmed speakers

Venue

The workshop takes place at Osaka University Nakanoshima Center (4-3-53 Nakanoshima, Kita-ku, Osaka 530-0005, Japan) from March 11 to March 13, 2025. You can easily reach the venue by walking:

  • 5 min. from Nakanoshima Station on the Keihan Nakanoshima Line
  • 9 min. from Fukushima Station on the Hanshin Main Line
  • 9 min. from Shin-Fukushima Station on the JR Tozai Line
  • 10 min. from Higobashi Station on the Osaka Metro Yotsubashi Line
  • 12 min. from Fukushima Station on the JR Loop Line
  • 16 min. from Yodoyabashi Station on the Osaka Metro Midosuji Line
  • 25 min. from Osaka Station on the JR Lines
  • 25 min. from Umeda Station on the Hankyu Line

The map uses Google Maps.

Organizers

Disclaimer

The title picture shows Osaka Castle and has been published on Wikipedia under the CC BY 2.5 Deed license.