[Seminario] INVITACIÓN Seminario Aprendizaje de Máquinas, lunes 15 de enero del 2018 a las 12:00 hrs.

[Maria Ines]

Estimados Académicos y Alumnos,

Se les invita para el próximo lunes 15 de enero a las 12:00 hrs, al 
Seminario  Aprendizaje de Máquinas,  que tendrá lugar en la sala de 
seminarios  John Von Neumann del CMM, ubicada en  Beauchef 851, torre 
norte, piso 7.
*
Seminario Aprendizaje de Máquinas

Expositor
David Sondak
Departamento: Institute for Applied Computational Science (IACS)
Harvard University

Sitio web: https://iacs.seas.harvard.edu/people/david-sondak

Título
*
*
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*Physics-based Models for Uncertainty Quantification in Chemical Kinetics*
*
  Abstract:*

Prediction is a core element of science and engineering. Sophisticated 
mathematical models
exist to make predictions in a variety of physical contexts including 
materials science, fluid
mechanics, and solid mechanics. Most of these models do not have known 
analytical solutions.
Moreover, they are generally difficult to solve numerically. In order to 
perform numerically
tractable computations, researchers often try to develop reduced models 
that account for the
essential physics while doing away with the complexity of the full 
model. Development of
reduced models necessarily induces model errors and the impact of these 
errors on scientific
and engineering predictions must be assessed and quantified. Even the 
most sophisticated
mathematical models contain errors and uncertainties due to the limits 
of human knowledge.
The field of uncertainty quantification seeks to rigorously quantify 
uncertainties and assess their
impact on predictions in scientific and engineering applications.

This talk will begin by providing an overview of uncertainty 
quantification in the context of
scientific and engineering predictions. I will then discuss recent 
results on the development of in-
adequacy models for chemical kinetics with applications to turbulent 
combustion. In particular,
a new physics-based inadequacy model is introduced that accounts for 
model error between a
detailed chemical kinetics model and a reduced model. Limitations and 
extensions of the model
are discussed. If time permits, I will describe recent attempts to 
develop physics-aware machine
learning algorithms that may be able to learn model terms in reduced 
models. Examples from
fluid turbulence and astrophysics will be presented.


Lunes 15 de enero a las 12:00 hrs. Sala de Seminarios John Von  Neumann, 
7mo piso, torre norte, Beauchef 851.

Esperando contar con su presencia, les saluda,

Ma. Inés Rivera

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