Data e horário: terça-feira (16/09), 10h30 - 11h30
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Título: Learning Physics from Videos
Resumo: Sensing is a universal task in science and engineering. Downstream tasks from
sensing include learning dynamical models, inferring full state estimates of a system (system
identification), control decisions, and forecasting. These tasks are exceptionally challenging to
achieve with limited sensors, noisy measurements, and corrupt or missing data. Existing
techniques typically use current (static) sensor measurements to perform such tasks and
require principled sensor placement or an abundance of randomly placed sensors. In
contrast, we propose a SHallow REcurrent Decoder (SHRED) neural network structure which
incorporates (i) a recurrent neural network (LSTM) to learn a latent representation of the
temporal dynamics of the sensors, and (ii) a shallow decoder that learns a mapping between
this latent representation and the high-dimensional state space. By explicitly accounting for
the time-history, or trajectory, of the sensor measurements, SHRED enables accurate
reconstructions with far fewer sensors, outperforms existing techniques when more
measurements are available, and is agnostic towards sensor placement. In addition, a
compressed representation of the high-dimensional state is directly obtained from sensor
measurements, which provides an on-the-fly compression for modeling physical and
engineering systems. Forecasting is also achieved from the sensor time-series data alone,
producing an efficient paradigm for predicting temporal evolution with an exceptionally limited
number of sensors. In the example cases explored, including turbulent flows, complex spatio-
temporal dynamics can be characterized with exceedingly limited sensors that can be
randomly placed with minimal loss of performance.
Data e horário: a definir
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Título: TBA
Resumo: TBA
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Título: TBA
Resumo: TBA
Data e horário: a definir
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Título: TBA
Resumo: TBA
Data e horário: terça-feira (16/09), 11h30 - 12h30
Local: a definir
Chair: a definir
Título: Incorporating behavioral change and risk perception in epidemiological models
Resumo: We examine how behavioral changes in vaccinated people who do not develop immunity influence the dynamics of a directly transmitted disease and key indices such as the basic reproductive number and vaccine effectiveness. We propose a model that considers a vaccine with three facets of failure: ``take'', ``degree'', and ``duration''. Additionally, the behavioral change of non-immune vaccinated individuals is modeled through a parameter that adjusts their contact rate based on compliance with mitigation measures.
Our results allow us to visualize the role of behavioral change in various factors influencing disease transmission dynamics. First, we demonstrate the existence of a backward bifurcation common in models for not fully effective vaccines. Second, we define a behavioral index threshold, which serves as a key indicator for determining whether the disease persists due to behavioral effects. Finally, our results highlight that both the behavioral index and the initial value of the infected population can play a decisive role in determining whether vaccine effectiveness reaches negative values.
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Título: TBA
Resumo: TBA
Data e horário: quinta-feira (18/09), 11h30 - 12h30
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Chair: a definir
Título: Avanços recentes em métodos de Lagrangiano aumentado para otimização
não linear
Resumo: Os métodos de Lagrangiano Aumentado (LA) constituem uma classe
importante e amplamente utilizada de métodos para resolver problemas de otimização
não linear com restrições. O método clássico de LA usa uma sequência iterativa de
subproblemas que são consideravelmente mais fáceis de resolver. Pela sua definição
intrínseca, a análise de convergência do método de LA está diretamente relacionada
ao estudo das chamadas condições sequenciais de otimalidade (SOC). Nos últimos
anos, uma atenção especial tem sido dedicada à definição de SOCs mais fracas. Um
dos métodos de LA mais destacados e estudados é conhecido como "Algencan". Este
método tem excelentes propriedades teóricas e apresenta um comportamento
numérico robusto. Nesta palestra, abordaremos os avanços dos últimos anos sobre
os métodos de LA.