Seminar 09/06 – Minds/Storm Joint Seminar – Mégane Bati and Sinan Yıldırım

For the next STORM seminar, Mégane Bati and Sinan Yıldırım will give two presentations.

First presentation (French):
12:45-2:00pm, Friday 09/06, Salle des thèses – IRIT
Title: Coupling Conduction, Convection and Radiative Transfer in a Single Path-Space: Application to Infrared Rendering

Second presentation (English):
2:00-3:00pm, Friday 09/06, Salle des thèses – IRIT
Title: Markov Chain Monte Carlo for Bayesian Inference and its Applications


Coupling Conduction, Convection and Radiative Transfer in a Single Path-Space: Application to Infrared Rendering:
In the past decades, Monte Carlo methods have shown their ability to solve PDEs, independently of the dimensionality of the integration domain and for different use-cases (e.g. light transport, geometry processing, physics simulation). Specifically, the path-space formulation of transport equations is a key ingredient to define tractable and scalable solvers, and we observe nowadays a strong interest in the definition of simulation systems based on Monte Carlo algorithms. We also observe that, when simulating combined physics (e.g. thermal rendering from a heat transfer simulation), there is a lack of coupled Monte Carlo algorithms allowing to solve all the physics at once, in the same path space, rather than combining several independent MC estimators, a combination that would make the global solver critically sensitive to the complexity of each simulation space.
This brings to our proposal: a coupled, single path-space, Monte Carlo algorithm for efficient multi-physics problems solving. In this work, we combine our understanding and knowledge of Physics and Computer Graphics to demonstrate how to formulate and arrange different simulation spaces into a single path space. We define a tractable formalism for coupled heat transfer simulation using Monte Carlo, and we leverage the path-space construction to interactively compute multiple simulations with different conditions in the same scene, in terms of boundary conditions and observation time. We validate our proposal in the context of infrared rendering with different thermal simulation scenarios: e.g., room temperature simulation, visualization of heat paths within materials (detection of thermal bridges), heat diffusion capacity of thermal exchanger. We expect that our theoretical framework will foster collaboration and multidisciplinary studies. The perspectives this framework opens are detailed and we suggest a research agenda towards the resolution of coupled PDEs at the interface of Physics and Computer Graphics. Project page:

Markov Chain Monte Carlo for Bayesian Inference and its Applications:
Markov chain Monte Carlo is a family of Monte Carlo algorithms that are widely used for Bayesian Inference in many fields including signal processing. The talk will start with an introduction to Bayesian inference and Markov chain Monte Carlo (MCMC). I will especially talk about the use of MCMC in latent variable models and present a novel MCMC method for those models. As an example of applications of MCMC to problems in signal processing, I will explain our past work on Pseudo-Marginal MCMC Sampling for Image Segmentation Using Nonparametric Shape Priors.