SIAM Student Chapter Seminar: Difference between revisions

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*'''When:''' 3:30 pm
*'''When:''' Fridays at 1 PM unless noted otherwise
*'''Where:''' Zoom
*'''Where:''' 9th floor lounge (we will also broadcast the virtual talks on the 9th floor lounge with refreshments)
*'''Organizers:''' [http://www.math.wisc.edu/~xshen/ Xiao Shen]
*'''Organizers:''' Yahui Qu, Peiyi Chen, Shi Chen and Zaidan Wu
*'''Faculty advisers:''' [http://www.math.wisc.edu/~jeanluc/ Jean-Luc Thiffeault], [http://pages.cs.wisc.edu/~swright/ Steve Wright]  
*'''Faculty advisers:''' [http://www.math.wisc.edu/~jeanluc/ Jean-Luc Thiffeault], [http://pages.cs.wisc.edu/~swright/ Steve Wright]  
*'''To join the SIAM Chapter mailing list:''' email [mailto:siam-chapter+join@g-groups.wisc.edu siam-chapter+join@g-groups.wisc.edu].
*'''To join the SIAM Chapter mailing list:''' email [mailto:siam-chapter+join@g-groups.wisc.edu siam-chapter+join@g-groups.wisc.edu].
*'''Zoom link:''' https://uwmadison.zoom.us/j/97976615799?pwd=U2xFSERIcnR6M1Y1czRmTjQ1bTFJQT09
*'''Passcode:  281031'''


<br>
== Spring 2024 ==


== Fall 2020  ==
{| class="wikitable"
 
|+
{| cellpadding="8"
!Date
!align="left" | date
!Location
!align="left" | speaker
!Speaker
!align="left" | title
!Title
|-
|9/29
|Yu Feng (Math)
|''[[#9/29, Yu Feng (Math)|Phase separation in the advective Cahn--Hilliard equation]]''
|-
|-
|-
|10/14
|2/2
|Dongyu Chen (WPI)
|VV911
|''[[#10/14, Yuchen Dong (WPI)|A Half-order Numerical Scheme for Nonlinear SDEs with one-sided Lipschitz Drift and H\:{o}lder Continuous Diffusion Coefficients]]''
|Thomas Chandler (UW-Madison)
|Fluid–body interactions in anisotropic fluids
|-
|-
|3/8
|Ingraham 214
|Danyun He (Harvard)
|Energy-positive soaring using transient turbulent fluctuations
|-
|-
|10/28
|3/15
|Evan Sorensen (math)
|VV911&Zoom
|''[[#10/28, Evan Sorenson (math)|Unsupervised data classification via Bayesian inference]]''
|Xiaoyu Dong (UMich)
|Approximately Hadamard matrices and Riesz bases in frames
|-
|-
|3/22
|VV911&Zoom
|Mengjin Dong (UPenn)
|Advancing Alzheimer's Disease Research: Insights and Innovations in MRI-Based Progression Tracking
|-
|-
|4/5
|VV911
|Sixu Li (UW-Madison)
|TBD
|-
|-
|4/12
|VV911&Zoom
|Anjali Nair (UChicago)
|TBD
|-
|-
|11/23
|4/19
|Weijie Pang (McMaster University)
|VV911
|''[[#11/23, Weijie Pang (McMaster University)|Pandemic Model with Asymptomatic Viral Carriers and Health Policy]]''
|Jingyi Li (UW-Madison)
|-
|TBD
|-
|-
|5/3
|
|
|Bella Finkel (UW-Madison)
|TBD
|}
|}


== Abstracts ==
==Abstracts==
 
'''February 2, Thomas Chandler (UW-Madison):''' Fluid anisotropy, or direction-dependent response to deformation, can be observed in biofluids like mucus or, at a larger scale, self-aligning swarms of active bacteria. A model fluid used to investigate such environments is a nematic liquid crystal. In this talk, we will use complex variables to analytically solve for the interaction between bodies immersed in liquid crystalline environments. This approach allows for the solution of a wide range of problems, opening the door to studying the role of body geometry, liquid crystal anchoring conditions, and deformability. Shape-dependent forces between bodies, surface tractions, and analogues to classical results in fluid dynamics will also be discussed.
=== 9/29, Yu Feng (Math) ===
'''Phase separation in the advective Cahn--Hilliard equation'''
 
The Cahn--Hilliard equation is a classic model of phase separation in binary mixtures that exhibits spontaneous coarsening of the phases. We study the Cahn--Hilliard equation with an imposed advection term in order to model the stirring and eventual mixing of the phases. The main result is that if the imposed advection is sufficiently mixing then no phase separation occurs, and the solution instead converges exponentially to a homogeneous mixed state. The mixing effectiveness of the imposed drift is quantified in terms of the dissipation time of the associated advection-hyperdiffusion equation, and we produce examples of velocity fields with a small dissipation time. We also study the relationship between this quantity and the dissipation time of the standard advection-diffusion equation.
 
 
=== 10/14, Yuchen Dong (WPI) ===
'''A Half-order Numerical Scheme for Nonlinear SDEs with one-sided Lipschitz Drift and Hölder Continuous Diffusion Coefficients'''
 
We consider positivity-preserving explicit schemes for one-dimensional nonlinear stochastic differential
equations. The drift coefficients satisfy the one-sided Lipschitz condition, and the diffusion coefficients
are Hölder continuous. To control the fast growth of moments of solutions, we introduce several explicit
schemes including the tamed and truncated Euler schemes. The fundamental idea is to guarantee the
non-negativity of solutions. The proofs rely on the boundedness for negative moments and exponential of
negative moments. We present several numerical schemes for a modified Cox-Ingersoll-Ross model and a
two-factor Heston model and demonstrate their half-order convergence rate.
 
 
=== 10/28, Evan Sorensen (math) ===
''' Unsupervised data classification via Bayesian inference'''
 
Bayesian inference is a way of “updating” our current state of knowledge given some data. In this talk, I will discuss how one can use Bayesian inference to classify data into separate groups. Particularly, I will discuss an application of this to outlier detection in contamination control within semiconductor manufacturing. Time permitting, I will talk about some computational tools for these models.
 
 


=== 11/23, Weijie Pang (McMaster University) ===
'''March 8, Danyun He (Harvard University):''' The ability of birds to soar in the atmosphere is a fascinating scientific problem. It relies on an interplay between the physical processes governing atmospheric flows, and the capacity of birds to process cues from their environment and learn complex navigational strategies. Previous models for soaring have primarily taken advantage of thermals of ascending hot air to gain energy. Yet, it remains unclear whether energy loss due to drag can be overcome by extracting work from transient turbulent fluctuations. In this talk, I will present a recent work that we look at the alternative scenario of a glider navigating in an idealized model of a turbulent fluid where no thermals are present. First, I will show the numerical simulations of gliders navigating in a kinematic model that captures the spatio-temporal correlations of atmospheric turbulence. Energy extraction is enabled by an adaptive algorithm based on Monte Carlo tree search that dynamically filters acquired information about the flow to plan future paths. Then, I will demonstrate that for realistic parameter choices, a glider can navigate to gain height and extract energy from flow. Glider paths reflect patterns of foraging, where exploration of the flow is interspersed with bouts of energy extraction through localized spirals. As such, this work broadens our understanding of soaring, and extends the range of scenarios where soaring is known to be possible.


'''Pandemic Model with Asymptomatic Viral Carriers and Health Policy '''
'''March 15, Xiaoyu Dong (University of Michigan, Ann Arbor):''' An $n \times n$ matrix with $\pm 1$ entries which acts on $\R^n$ as a scaled isometry is called Hadamard. Such matrices exist in some, but not all dimensions. Combining number-theoretic and probabilistic tools we construct matrices with $\pm 1$ entries which act as approximate scaled isometries in $\R^n$ for all $n \in \N$. More precisely, the matrices we construct have condition numbers bounded by a constant independent of $n$.


By October 13, 2020, the total number of COVID-19 confirmed cases had been 37,880,040 with 1,081,857 death in the world. The speed, range and influence of this virus exceed any pandemic in history. To find reasons of this incredible fast spread, we introduce asymptomatic category into a SEIR pandemic model. Based on published data of Italy, we calibrated exposed rates of COVID-19 in this model and then simulated the spread of COVID-19 for different asymptomatic rates. To measure the effects of different types of public health policies on this pandemic, we construct a pandemic model including health policies. By the simulation of this model, we provide feasible suggestions of containment to regulators.  
Using this construction, we establish a phase transition for the probability that a random frame contains a Riesz basis. Namely, we show that a random frame in $\R^n$ formed by $N$ vectors with  independent identically distributed coordinate having a non-degenerate symmetric distribution contains many Riesz bases with high probability provided that $N \ge \exp(Cn)$. On the other hand, we prove that if the entries are subgaussian, then a random frame fails to contain a Riesz basis with probability close to $1$ whenever $N \le \exp(cn)$, where $c<C$ are constants depending on the distribution of the entries.




'''March 22, Mengjin Dong (University of  Pennsylvania)''': Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by memory loss, cognitive decline, and behavioral changes primarily in the elderly population. As the most prevalent form of dementia, it impacts millions of families globally. The pathological hallmarks of AD, such as abnormal protein build-up in the brain, can manifest decades before the onset of clinical symptoms. Neuroimaging modalities such as positron emission tomography (PET) and magnetic resonance imaging (MRI) play pivotal roles in studying disease progression and elucidating its underlying mechanisms.


<br>
In this presentation, I will commence with an overview of AD fundamentals and recent research advancements. Subsequently, I will delve into my research, which utilizes deep learning techniques to longitudinally monitor and localize AD progression using MRI data.


== Past Semesters ==
==Past Semesters==
*[[SIAM Fall 2023]]
*[[SIAM Spring 2023]]
*[[SIAM Seminar Fall 2022|Fall 2022]]
*[[Spring 2022 SIAM|Spring 2022]]
*[[SIAM Student Chapter Seminar/Fall2021|Fall 2021]]
*[[SIAM_Student_Chapter_Seminar/Fall2020|Fall 2020]]
*[[SIAM_Student_Chapter_Seminar/Fall2020|Fall 2020]]
*[[SIAM_Student_Chapter_Seminar/Spring2020|Spring 2020]]
*[[SIAM_Student_Chapter_Seminar/Spring2020|Spring 2020]]

Latest revision as of 19:28, 18 March 2024


Spring 2024

Date Location Speaker Title
2/2 VV911 Thomas Chandler (UW-Madison) Fluid–body interactions in anisotropic fluids
3/8 Ingraham 214 Danyun He (Harvard) Energy-positive soaring using transient turbulent fluctuations
3/15 VV911&Zoom Xiaoyu Dong (UMich) Approximately Hadamard matrices and Riesz bases in frames
3/22 VV911&Zoom Mengjin Dong (UPenn) Advancing Alzheimer's Disease Research: Insights and Innovations in MRI-Based Progression Tracking
4/5 VV911 Sixu Li (UW-Madison) TBD
4/12 VV911&Zoom Anjali Nair (UChicago) TBD
4/19 VV911 Jingyi Li (UW-Madison) TBD
5/3 Bella Finkel (UW-Madison) TBD

Abstracts

February 2, Thomas Chandler (UW-Madison): Fluid anisotropy, or direction-dependent response to deformation, can be observed in biofluids like mucus or, at a larger scale, self-aligning swarms of active bacteria. A model fluid used to investigate such environments is a nematic liquid crystal. In this talk, we will use complex variables to analytically solve for the interaction between bodies immersed in liquid crystalline environments. This approach allows for the solution of a wide range of problems, opening the door to studying the role of body geometry, liquid crystal anchoring conditions, and deformability. Shape-dependent forces between bodies, surface tractions, and analogues to classical results in fluid dynamics will also be discussed.

March 8, Danyun He (Harvard University): The ability of birds to soar in the atmosphere is a fascinating scientific problem. It relies on an interplay between the physical processes governing atmospheric flows, and the capacity of birds to process cues from their environment and learn complex navigational strategies. Previous models for soaring have primarily taken advantage of thermals of ascending hot air to gain energy. Yet, it remains unclear whether energy loss due to drag can be overcome by extracting work from transient turbulent fluctuations. In this talk, I will present a recent work that we look at the alternative scenario of a glider navigating in an idealized model of a turbulent fluid where no thermals are present. First, I will show the numerical simulations of gliders navigating in a kinematic model that captures the spatio-temporal correlations of atmospheric turbulence. Energy extraction is enabled by an adaptive algorithm based on Monte Carlo tree search that dynamically filters acquired information about the flow to plan future paths. Then, I will demonstrate that for realistic parameter choices, a glider can navigate to gain height and extract energy from flow. Glider paths reflect patterns of foraging, where exploration of the flow is interspersed with bouts of energy extraction through localized spirals. As such, this work broadens our understanding of soaring, and extends the range of scenarios where soaring is known to be possible.

March 15, Xiaoyu Dong (University of Michigan, Ann Arbor): An $n \times n$ matrix with $\pm 1$ entries which acts on $\R^n$ as a scaled isometry is called Hadamard. Such matrices exist in some, but not all dimensions. Combining number-theoretic and probabilistic tools we construct matrices with $\pm 1$ entries which act as approximate scaled isometries in $\R^n$ for all $n \in \N$. More precisely, the matrices we construct have condition numbers bounded by a constant independent of $n$.

Using this construction, we establish a phase transition for the probability that a random frame contains a Riesz basis. Namely, we show that a random frame in $\R^n$ formed by $N$ vectors with  independent identically distributed coordinate having a non-degenerate symmetric distribution contains many Riesz bases with high probability provided that $N \ge \exp(Cn)$. On the other hand, we prove that if the entries are subgaussian, then a random frame fails to contain a Riesz basis with probability close to $1$ whenever $N \le \exp(cn)$, where $c<C$ are constants depending on the distribution of the entries.


March 22, Mengjin Dong (University of Pennsylvania): Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by memory loss, cognitive decline, and behavioral changes primarily in the elderly population. As the most prevalent form of dementia, it impacts millions of families globally. The pathological hallmarks of AD, such as abnormal protein build-up in the brain, can manifest decades before the onset of clinical symptoms. Neuroimaging modalities such as positron emission tomography (PET) and magnetic resonance imaging (MRI) play pivotal roles in studying disease progression and elucidating its underlying mechanisms.

In this presentation, I will commence with an overview of AD fundamentals and recent research advancements. Subsequently, I will delve into my research, which utilizes deep learning techniques to longitudinally monitor and localize AD progression using MRI data.

Past Semesters