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BEGIN:VEVENT
SUMMARY:Eric Stone (ANU)
DTSTART:20220510T060000Z
DTEND:20220510T070000Z
DTSTAMP:20260422T225820Z
UID:anumacs/1
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/anumacs/1/">
 Stories of mathematics and computational science in genetic mapping</a>\nb
 y Eric Stone (ANU) as part of ANU Mathematics and Computational Sciences S
 eminar\n\nLecture held in Room 1.33\, Hanna Neumann Building #145.\n\nAbst
 ract\nTraits of interest often vary within a population\, leading biologis
 ts to investigate the genetic basis of that observed variation. This can b
 e done directly via an association study\, in which one of many methods is
  used to identify correlational patterns that link genetic variation to tr
 ait variation. Alternatively\, in experimental systems\, individuals can b
 e selectively bred to create a “genetic mapping population” with a mor
 e desirable signal-to-noise ratio. In this talk\, I will share my experien
 ce creating mapping populations as a vehicle to introducing some of the ma
 thematical and computational challenges that have ensued. I will discuss c
 ombinational and probabilistic issues that arise in ideal populations\, co
 ntrasted by some algorithmic concerns that arise in natural populations. M
 y goal is to provide a sampling of accessible problems in mathematics and 
 computational science encountered in a practical biological context.\n
LOCATION:https://researchseminars.org/talk/anumacs/1/
END:VEVENT
BEGIN:VEVENT
SUMMARY:David Ham (Imperial College London)
DTSTART:20220524T060000Z
DTEND:20220524T070000Z
DTSTAMP:20260422T225820Z
UID:anumacs/2
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/anumacs/2/">
 Automating forward and inverse finite element simulation in Firedrake and 
 Dolfin-adjoint</a>\nby David Ham (Imperial College London) as part of ANU 
 Mathematics and Computational Sciences Seminar\n\nLecture held in Room 1.3
 3\, Hanna Neumann Building #145.\n\nAbstract\nSimulating continuous system
 s modelled by PDEs underpins much of computational science and engineering
 . Each simulation is a complex combination of PDEs\, parametrisations\, di
 scretisations\, preconditioners and solvers. The precise combination that 
 is optimal is different for each application and changes with the hardware
 \, or as further advances in numerical mathematics are made. Many (possibl
 y most) simulation challenges in science and engineering are actually inve
 rse problems in which parameters are sought\, sensitivities analysed and/o
 r data assimilated.\n\nHere I will present Firedrake\, an automated system
  for generating numerical solutions to PDEs from a high level mathematical
  specification. I will examine some of the capabilities of the system befo
 re lifting the lid on the sequence of automated mathematical transformatio
 ns that make it possible. I will also cover the interaction with dolfin-ad
 joint to produce gradients of solution functionals by solving the adjoint 
 PDE.\n
LOCATION:https://researchseminars.org/talk/anumacs/2/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Paul Malcolm (ANU DST)
DTSTART:20220607T060000Z
DTEND:20220607T070000Z
DTSTAMP:20260422T225820Z
UID:anumacs/3
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/anumacs/3/">
 New representations for a semi-Markov chain and related filters</a>\nby Pa
 ul Malcolm (ANU DST) as part of ANU Mathematics and Computational Sciences
  Seminar\n\nLecture held in Room 1.33\, Hanna Neumann Building #145.\n\nAb
 stract\nIt is now usual that the null-hypothesis for a finite-state stocha
 stic process is conveniently taken to be the standard Markov chain. In the
  absence of any other system knowledge this is the model that is often use
 d. Some reasons for this are\; Markov chains are relatively simple\, they 
 have been well studied and much is known about these processes. Added to t
 his there are now decades of history applying the standard Hidden Markov M
 odel (HMM) to: defence science\, gene sequencing\, health science\, machin
 e learning\, artificial intelligence and many other areas. In this seminar
  we will briefly recall two common application domains of estimation with 
 latent Markov processes\, 1) parts-of-speech tagging (POS) in natural lang
 uage processing and 2) tracking a maneuvering object with a Jump Markov Sy
 stem. Semi-Markov models relax an implicit feature of every state in a fir
 st-order time-homogeneous Markov chains\, that is\, the sojourn random var
 iables of such states are geometrically distributed and are therefore\, (u
 niquely) memoryless random variables. In contrast\, semi-Markov chains all
 ow arbitrary sojourn models. Consequently\, a Hidden semi-Markov Model (Hs
 MM) offers a richer class of model\, but retains the classical HMM as a sp
 ecial degenerate case.\n\nThe main task we address in this seminar concern
 s model calibration\, or parameter estimation of a HsMM. We develop an Exp
 ectation Maximization (EM) algorithm to compute the best fitting (in the M
 aximum Likelihood sense) HsMM for a given set of observation data. There a
 re several parts to this task\, the first is to derive a recursive filter 
 and smoother for a partially observed semi-Markov chain. The second and mo
 re challenging part of the task is to derive filters and smoothers for var
 ious processes derived from the latent semi-Markov chain\, for example\, a
  counting process that counts the number of transitions between two distin
 ct states labelled "i" and "j"\, up to an including time k. We will see th
 at estimators for such quantities are non-trivial\, largely because of the
  sojourn dependence in transition probabilities.\n\nThe estimators we pres
 ent are all for partially observed joint events\, that is\, the state of t
 he semi-Markov chain at time "k" and the cumulative time it has remained i
 n this state. This means we are assured of exponential forgetting of initi
 al conditions in our estimators. Separate estimators for individual quanti
 ties such as the semi-Markov state alone are easily computed via marginali
 zation.\n
LOCATION:https://researchseminars.org/talk/anumacs/3/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Quanling Deng (ANU)
DTSTART:20220802T060000Z
DTEND:20220802T070000Z
DTSTAMP:20260422T225820Z
UID:anumacs/4
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/anumacs/4/">
 Superparameterisation of Arctic sea ice floes</a>\nby Quanling Deng (ANU) 
 as part of ANU Mathematics and Computational Sciences Seminar\n\nLecture h
 eld in Room 1.33\, Hanna Neumann Building #145.\n\nAbstract\nIn this talk\
 , I will start with some quick facts about Arctic sea ice floes and then g
 ive a quick review of the evolution of sea ice models. The first models ar
 e Eulerian continuum models that describe the sea ice floes as viscous-pla
 stics (Hilber 1979). Lagrangian particle models have been developed recent
 ly\, showing improved model performance\, especially in ice-marginal zones
  where sea ice is fragmented. The most successful one is the discrete elem
 ent method (DEM). It characterises the physical quantities of each sea ice
  floe along its trajectory under the Lagrangian coordinates. The major cha
 llenges are 1) model coupling in different frames of reference (Lagrangian
  for sea ice while Eulerian for the ocean and atmosphere dynamics)\; 2) th
 e heavy computational cost when the number of the floes is large\; and 3) 
 inaccurate floe parameterisation when the floe distribution has multiscale
  features. In this talk\, I will present a superfloe parameterisation to r
 educe the computational cost and a superparameterisation to capture the mu
 ltiscale features. The superfloe parameterisation algorithm generates a sm
 all number of superfloes that effectively approximate a considerable numbe
 r of the floes. The parameterisation scheme satisfies several important ph
 ysics constraints that guarantee similar short-term dynamical behaviour wh
 ile maintaining long-range uncertainties\, especially the non-Gaussian sta
 tistical features\, of the full system. In addition\, the superfloe parame
 terisation facilitates noise inflation in data assimilation that recovers 
 the unobserved ocean field underneath the sea ice. To capture the multisca
 le features\, we follow the derivation of the Boltzmann equation for parti
 cles and superparameterise the sea ice floes as continuity equations gover
 ning the statistical moments of mass density and linear and angular veloci
 ties. This leads to a particle-continuum coupled model. The continuum part
  captures the large scales and the particle part captures the small scales
 . The particle model is localised and fully parallelised for computation e
 fficiency. I will present several numerical experiments to demonstrate the
  success of the proposed schemes. This is joint work with Nan Chen (UW-Mad
 ison) and Sam Stechmann (UW-Madison).\n
LOCATION:https://researchseminars.org/talk/anumacs/4/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Minh Bui (ANU)
DTSTART:20220705T060000Z
DTEND:20220705T070000Z
DTSTAMP:20260422T225820Z
UID:anumacs/5
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/anumacs/5/">
 Phylogenetic inference in the genomic era</a>\nby Minh Bui (ANU) as part o
 f ANU Mathematics and Computational Sciences Seminar\n\nLecture held in Ro
 om 1.33\, Hanna Neumann Building #145.\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/anumacs/5/
END:VEVENT
BEGIN:VEVENT
SUMMARY:John Taylor (CSIRO)
DTSTART:20220712T060000Z
DTEND:20220712T070000Z
DTSTAMP:20260422T225820Z
UID:anumacs/6
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/anumacs/6/">
 TBC</a>\nby John Taylor (CSIRO) as part of ANU Mathematics and Computation
 al Sciences Seminar\n\nLecture held in Room 1.33\, Hanna Neumann Building 
 #145.\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/anumacs/6/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Yu Lin (ANU)
DTSTART:20220719T060000Z
DTEND:20220719T070000Z
DTSTAMP:20260422T225820Z
UID:anumacs/7
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/anumacs/7/">
 Solving Genome Puzzles</a>\nby Yu Lin (ANU) as part of ANU Mathematics and
  Computational Sciences Seminar\n\nLecture held in Room 1.33\, Hanna Neuma
 nn Building #145.\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/anumacs/7/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Giuseppe Barca (ANU)
DTSTART:20220816T060000Z
DTEND:20220816T070000Z
DTSTAMP:20260422T225820Z
UID:anumacs/8
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/anumacs/8/">
 Towards Exascale Computational Quantum Mechanics</a>\nby Giuseppe Barca (A
 NU) as part of ANU Mathematics and Computational Sciences Seminar\n\nLectu
 re held in Room 1.33\, Hanna Neumann Building #145.\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/anumacs/8/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Linda Stals (ANU)
DTSTART:20220913T060000Z
DTEND:20220913T070000Z
DTSTAMP:20260422T225820Z
UID:anumacs/10
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/anumacs/10/"
 >Fault Tolerant Iterative Solvers</a>\nby Linda Stals (ANU) as part of ANU
  Mathematics and Computational Sciences Seminar\n\nLecture held in Room 1.
 33\, Hanna Neumann Building #145.\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/anumacs/10/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Vanessa Robins (ANU)
DTSTART:20220927T060000Z
DTEND:20220927T070000Z
DTSTAMP:20260422T225820Z
UID:anumacs/11
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/anumacs/11/"
 >Topological Data Analysis</a>\nby Vanessa Robins (ANU) as part of ANU Mat
 hematics and Computational Sciences Seminar\n\nLecture held in Room 1.33\,
  Hanna Neumann Building #145.\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/anumacs/11/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Charles O'Neill & Jack Miller (ANU)
DTSTART:20220823T060000Z
DTEND:20220823T070000Z
DTSTAMP:20260422T225820Z
UID:anumacs/12
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/anumacs/12/"
 >Eigenvalue initialisation and regularisation for koopman autoencoders and
  beyond</a>\nby Charles O'Neill & Jack Miller (ANU) as part of ANU Mathema
 tics and Computational Sciences Seminar\n\nLecture held in Room 1.33\, Han
 na Neumann Building #145.\n\nAbstract\nRecent efforts have been made to le
 arn the Koopman operator with predictive autoencoders. However\, little at
 tention has been payed to the initialisation of these networks. Noting the
  importance of eigenvalues to the action of a linear operator\, one may as
 k whether it would be useful to employ them in the initialisation and regu
 larisation of these autoencoders? To answer this\, we devise a spectral ei
 genvalue initialisation and eigenvalue penalty scheme. Having done so\, we
  discover that eigenvalues do in fact have great utility for this purpose.
  We demonstrate that in learning a Koopman operator for a damped driven pe
 ndulum\, appropriate initialisation and regularisation can improve initial
  performance by an order of magnitude. We also show with this system that 
 as the dissipative element of a dynamical system decreases\, the utility o
 f unit circle initialisation schemes increase and the utility of different
  regularisation schemes change. Additionally\, we show that the benefits o
 f eigenvalue initialisation and regularisation generalise to real-world cy
 clone wind data\, sea surface temperature prediction and flow over a cylin
 der.\n
LOCATION:https://researchseminars.org/talk/anumacs/12/
END:VEVENT
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