[Physics-grads-open] Physics & Astronomy Student Seminar (PASS), October 14 (Fri) at 4 pm: Virtual

[Ming-Feng Ho]

Hi all,

A reminder we will do a virtual talk *today at 4 pm PST (*
https://ucr.zoom.us/j/94967485304?pwd=WFczZnpVcWpkR3ZVZVBETmdTNFI0UT09*)*.

Today I will give an update on my recent work on applying machine learning
to train on cosmological simulations from different box sizes and different
resolutions.

We have also invited the following presenters for this Fall:
10/21: TBA
10/28: *Reza Monadi* (UCR, Astrophysics, PI: Simeon Bird)
11/4:   *Wei-Cheng Liao* (UCR, Condensed Matter Experiment, PI: Jing Shi)
11/11:* Robert Dawson* (UCR, Condensed Matter Theory, PI: Aji Vivek)
11/18: *Wenjun Chang* (UCR, Astrophysics, PI: Gillian Wilson)
11/25: Holiday
12/2 and 12/9: TBA

More information will come soon, stay tuned!
Ming-Feng





On Wed, Oct 12, 2022 at 11:21 AM Ming-Feng Ho <mingfeng.ho at email.ucr.edu>
wrote:

> Hi Physics and Astronomy Grads!
>
> I am happy to announce we will resume the *P*hysics & *A*stronomy *S*tudent
> *S*eminar (PASS) *this Friday (Oct 14)*.
>
> PASS is a student-run and -oriented seminar, providing graduate students a
> safe place to practice their job talks and qualify exams. PASS is also a
> safe place to practice asking questions and being asked questions.
>
> We have been running PASS since 2022 Spring, and have already helped
> several students on practicing their job talks. The past program could be
> found here
> <https://jibancat.github.io/ucr-student-seminars/program/index.html>.
>
> This quarter, I am happy to be the first presenter:
>
>
> *Title: MF-Box: Multi-fidelity emulation for simulations from different
> box sizes using a graphical Gaussian process*
>
>
> *Ming-Feng Ho (University of California, Riverside)*
>
> *Abstract: *Bayesian inference is necessary for any scientific
> discipline, especially for explaining cosmological and astrophysical
> observations. However, Bayesian inference also introduces the computational
> burden of >10^6 likelihood evaluations on the theoretical model. Emulation
> is the current only way to perform Bayesian inference using accurate
> numerical simulations. By approximating the input/output relationship of
> numerical simulations using a machine learning model, emulators work as
> cheap surrogates, allowing fast likelihood evaluations suitable for
> inference using Markov Chain Monte Carlo (MCMC). Nevertheless, as the
> experiments become highly precise in the future, the training data for an
> accurate emulator will also become impossible to generate, making the
> emulator approach impractical. In this talk, I will present MF-Box, a
> multi-fidelity emulation framework that allows us to combine cosmological
> simulations from different resolutions and different box sizes.
> Multi-fidelity emulation augments expensive high-resolution simulations
> with many cheap low-resolution simulations, minimizing the computational
> budget. MF-Box extends our previous multi-fidelity emulator, allowing the
> machine learning model to use simulations from different box sizes to
> accelerate the training via transfer learning. I will also present a
> theoretical approach to analyzing the emulator's performance and
> determining the optimal budget allocation.
>
>
> *Friday, October 14, 2022 at 4 pmVirtual via Zoom:*
> https://ucr.zoom.us/j/94967485304?pwd=WFczZnpVcWpkR3ZVZVBETmdTNFI0UT09
>
>
>
> PS, We need more speakers for Fall! *If you want to recommend or
> volunteer, write the speaker's name in this sheet
> <https://docs.google.com/spreadsheets/d/13Y7qZKoB8Qzy44ZukZWKcU_nF9U6MnLAIOj51bEcVP4/edit?usp=sharing>.*
> We will contact the speakers to confirm the date.
>
> Hope to see you there!
> Ming-Feng Ho
> on behave of PASS organizing team
>
> ---
> Ming-Feng Ho
> PhD Student
> Physics and Astronomy, UCR
> NASA FINESST FI
> jibancat.github.io
>
>
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