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Upcoming seminars

Semi-automatic Calculations of Multi-loop Feynman Amplitudes with AmpRed

Wen Chen, South China Normal University

Today @ 12pm

An OpenFOAM-based solver for modeling low Mach number turbulent flows at high pressure with real-fluid effects

Dr. Danh Nam Nguyen, Ulsan National Institute of Science and Technology

Today @ 12pm

Engineering Practices for Composite Aircraft Structural Damage Tolerance and Aging

Dr. Larry Ilcewicz, Federal Aviation Administration

Today @ 2pm

The puzzles of g-2: an update

Thomas Teubner, University of Liverpool

Tomorrow @ 6am

Faster and objective Level Set-DEM mechanical simulations of discrete systems with convex particles from contact history and particle surface considerations

Jérôme Duriez, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement

Tomorrow @ 12pm

How Chinese-English Speakers Processing Metaphorical Actions

Mingming Li, Victoria University of Wellington

Fri @ 12:10am

Recent seminars

Bridging the gap: unravelling plant centromeres in the telomere-to-telomere era

Matthew Naish, University of Warwick

10.52843/cassyni.9stf6j

A Novel Method for Fast, Accurate and Stable Computations of Turbulent Flows with Strongly Rotation and Stratification and Convection with Application to Jupiter's Great Red Spot

Philip Stephen Marcus, University of California, Berkeley

Sep 30

Uterine Rhythms: navigating the changes during the estrus cycle

Mr Mathias Roesler, University of Auckland

Sep 30

Darwinian Quantum Gravity dynamics of small particles

Nicolas Francisco Lori, University of Minho

10.52843/cassyni.hvt4fm

From Physics-Informed Machine Learning to Physics-Informed Machine Intelligence: Quo Vadimus?

Prof. George Karniadakis, Brown University
Prof. Giuseppe Quaranta, Sapienza University of Rome

Sep 29

Arbitrage in automated market makers

Prof. N. Dimitri, University of Siena

Sep 29

Seminar highlights

TBLT Pilot PAR: Thematic findings
Benefits: improved student participation, positive feedback
Teacher Nhan: "With that survey task, I saw that students were more
creative and interactive. Compared to the previous true/false exercises, students did the exercise without thinking, but with this exercise, I saw
that they did it very actively. At first, I thought it was difficult to ask for a summary report on the survey, but after the instructions were finished,
students knew how to collect data from the questions in the section above. Then they synthesised it into a pretty good summary." (Nhan-VS-PRD)
Teacher Tinh: "In my class, I've noticed the students are more active in
speaking and are also more cooperative and cheerful." (Tinh-PRD)
(translated from Vietnamese in all cases)

Problem setup
Karthik Duraisam
AEROSPACE
ENGINEERING
UNIVERSITY OF MICHIGAN
R(q) = O.
Ground truth: Navier--Stokes
Problem Setup
Peculiarities
Proof-of-concept
R(q) = O
R(q) + N(F(q)) (q)) = O
Coarse-grained
Ground truth
Model consistency
Guidelines
Examples
R(αₘ) + N(Fm(qm,ₛₘ), (qₘ, ₅ₘ)) = 0
Philosophies
Coarse-grained
Model
Gm (5ₘ, qm) = 0
0/93100

Motivation - Firm Involvement in Science
Known: why firms invest in science research (Rosenberg 1990; Rotolo et al 2021;
Ahmed 2023; Shvadron 2023).
Unknown: effect of firm involvement on scientific quality (i.e. how useful it is
for other science / "foundational").
Important: how to think about current policy and strategy topics: top-talent "brain drains" (Gofman and Jin 2023), declining corporate involvement in science
(Arora et al 2017; 2020), and optimal government funding of science (Ho et al 2021).
Literature expectations are murky.
Skepticism. Profit-motivated Firms
Lots of Examples. Many foundational
redirect researchers to more solve
breakthroughs in science from firms.
specific, commercial problems.
(Transistor, CMBR, High-Temp
(Aghion et al 2008; Lacetera and
Superconductivity, Sequencing the
Zirulia 2012)
Genome, Artificial Intelligence)

Outline
Modleing money as a formal language
This introduces fundamental uncertainties in the economy
These uncertainties explain the main functions of money
Logica Universalis Webinar

Transmitted variation
Tim Davis
Response variable y
y = f (x)
dy ≡ σγ
σ₂ ≡ 2 σ²
There are two actions to decrease
the transmitted variation σу:
Either
Design variable, x
1. decrease σₓ
Genichi Taguchi reversed the usual order of doing things:-
Instead of getting the design on target and then reducing the variation, he
2. decrease
suggested reducing variation first and then getting the design on target (he
embedded this approach into his parameter design methodology).
Remark: if f (x) is not known explicitly, the gradients dy at each value of x
will need to be determined numerically (this is the usual practice in
computer experiments)

$Conclusion WIEN SIGNATURE Finite difference code for Direct Numerical Simulation of multiphase turbulence Conservative Allen-Cahn phase-field formulation (ACDI) Code - MHIT36 coupled with Navier-Stokes equations GPU acceleration via OpenACC and cuFFT, with scalable data management through cuDecomp Excellent scalability (up to 1024 GPUs) on multiple HPC systems Validated for https://github.com/Multiphase Single-phase turbulence FlowLab/MHIT36 Multiphase emulsions Future Work Simulation of systems with high volume fractions Integration of modules for surfactant transport Implementation of multi-marker methods$