Speaker
Samuel
Wong
|
Department
of
Statistics
and
Actuarial
Science,
University
of
Waterloo
https://swong.ca/index.html
Title
Inference of Dynamic Systems from Noisy and Sparse Data via Manifold-constrained Gaussian Processes
Abstract
Ordinary differential
equations
are
a
ubiquitous
tool
for
modeling
behaviors
in
science, such
as
gene
regulation,
epidemics
and
ecology.
An
important
problem
is
to infer
and
characterize
the
uncertainty
of parameters
that
govern
the
equations. In
this
talk
I
will
present
an
accurate
and
fast
inference
method
using manifold-constrained
Gaussian
processes,
such
that
the
derivatives
of
the Gaussian
process
must satisfy
the
dynamics
of
the
differential
equations.
Our method
completely
avoids
the
use
of
numerical
integration
and
is
thus
fast
to compute.
Our
construction
is
embedded
in
a
principled
statistical framework
and is
demonstrated
to
yield
fast
and
reliable
inference
in
a
variety
of
practical problems,
including
when
a
system
component
is
unobserved.
This
is
joint
work
with
Shihao
Yang
(Georgia
Tech)
and
Samuel
Kou
(Harvard).