Master-Seminar - Deep Learning in Physics (IN2107, IN0014)

Prof. Dr. Nils Thuerey

Virtual Seminar

BigBlueButton

• For our seminar we use BigBlueButton: https://bigbluebutton.org/html5/
• Please remain muted unless you have permission to speak. 
• If you have a question or comment, please let us know in the chat, we will let you know as soon as you can speak, or post it directly in the chat.

Hardware Setup

A Laptop with built in microphone and speakers is terrible for everyone else in the virtual meeting!

• Laptop fan makes noise (fan will most probably rev up after a couple of minutes, especially if you share your screen and if there are many users in the virtual meeting).
• Typing and/or touchpad sounds will be transferred to communication partners.
• Most likely there will be echo/feedback from the speakers.
• Use the headset that came with your mobile phone. Even the cheapest headset will perform better than the built in microphone in a notebook.
• (Cheap) bluetooth headsets are okay as well, but they perform not as good as wired ones!
• Use an external microphone in combination with headphones. Some webcams have good quality microphones built in (e.g. Logitech).
• Tablets like Apple iPads have quite solid built in microphones (and no fan). The same is possibly true for other tablets and even for smart phones. If you do not have a better alternative, they will most probably perform better than a Laptop.

Giving a Talk Online

Giving a talk in a virtual meeting scenario is quite different from giving a talk in a classroom scenario. You do not get any feedback from the audience during the talk (do they look happy/bored/satisfied?) and you cannot interact as easily in a non-verbal way with the audience as in the classroom, e.g. by pointing to things displayed on the projector screen. For this reason, mentally prepare yourself to talking to your screen for 20-30 minutes!

Normally, your slides should not contain too much written text. In the case of virtual meetings, some more information than usual is not wrong. Added info can help the audience to bridge gaps in case there was a glitch in the network connection, etc. Lastly, it is a good idea to open the content of the slides step by step to avoid that the audience can read faster than you talk.

Normally, when you give a talk, you are standing. This creates some tension in you, you will sound more energetic compared to talking while sitting, and, lastly, it helps you to concentrate. So it is a good idea to create a setup for giving the talk where you can stand in front of your screen.

You are supposed to give a talk. So do not write a script that you read to the audience. Reading out a pre-made script would be boring in a classroom scenario and it will be as boring in the virtual meeting!

Also important: switch off any type of messengers, notifications, etc. which can distract you. As you are pretty much isolated from the audience, it is a good idea to set a timer to keep track of elapsed time. Lastly, prepare for technical problems. Have some kind of second channel open to your advisor that can be used to contact you in case something unexpected happens.

Especially if you are using a quite “wide” (omnidirectional) microphone that sits on your desk, please avoid loud sounds from typing on your keyboard when you flip through slides while talking.

Papers

No	Date	Presenter	Paper	Advisor
01	16.05	G. Prastalo	Accelerating Eulerian Fluid Simulation With Convolutional Networks	Lukas
02	16.05	R. Koprinkov	Learning to Simulate Complex Physics with Graph Networks	Benjamin
16	16.05	A. Sleimi	Purely data-driven medium-range weather forecasting achieves comparable skill to physical models at similar resolution	Nilam
18	23.05	A. Xavier	Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows	Lukas
19	23.05	B. Nederkorn	Data-driven nonlinear aeroelastic models of morphing wings for control	Nilam
12	23.05	J. Tandberg	Deep learning methods for super-resolution reconstruction of turbulent flows	Benjamin
03	30.05	N. Wallat	Physics Informed Deep Learning: Data-driven Solutions of Nonlinear Partial Differential Equations	Lukas
04	30.05	L. Q. Nguyen	Learning data-driven discretizations for partial differential equations	Nilam
06	30.05	O. N. M. Al-Dabooni	Neural Ordinary Differential Equations	Benjamin
13	13.06	B. Börcsök	Learning to control PDEs with differentiable physics	Lukas
09	13.06	G. Ekaterina	Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction	Benjamin
05	13.06	T. B. P. Nguyen	Discovering physical concepts with neural networks	Nilam
20	20.06	P. Ding	tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow	Lukas
28	20.06	L. Gökalpay	CFDNet: a deep learning-based accelerator for fluid simulations	Nilam
10	20.06	I. Alvian	Machine learning accelerated computational fluid dynamics	Nilam
33	27.06	S. Ben Mohamed	Learning to Assimilate in Chaotic Dynamical Systems	Benjamin
32	27.06	Y. A. Tunali	Multiwavelet-based Operator Learning for Differential Equations	Benjamin
27	27.06	R. Ayari	Shape As Points: A Differentiable Poisson Solver	Lukas
15	04.07	P. Ferreira dos Santos Saraiva	Deep learning and the Schrödinger equation	Nilam
07	04.07	Y. Chen	Hamiltonian Neural Networks	Benjamin
26	04.07	K. Mio	Learned Coarse Models for Efficient Turbulence Simulation	Benjamin
08	-	-	Lagrangian Fluid Simulation with Continuous Convolutions	-
11	-	-	Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers	-
14	-	-	Assessment of unsteady flow predictions using hybrid deep learning based reduced-order models	-
17	-	-	Model identification of reduced order fluid dynamics systems using deep learning	-
21	-	-	Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations	-
22	-	-	Solving high-dimensional partial differential equations using deep learning	-
23	-	-	Transfer learning for nonlinear dynamics and its application to fluid turbulence	-
24	-	-	SPNets: Differentiable Fluid Dynamics for Deep Neural Networks	-
25	-	-	E(n) Equivariant Graph Neural Networks	-
29	-	-	Towards Physics-informed Deep Learning for Turbulent Flow Prediction	-
30	-	-	LSH-SMILE: Locality Sensitive Hashing Accelerated Simulation and Learning	-
31	-	-	Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features	-

You can access the papers through TUM library's eAccess.