The MIT chapter of the Society for Applied and Industrial Mathematics (SIAM) welcomes undergraduate and graduate students interested in applied mathematics and computational science.

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Recent + Upcoming
## Jun 19, 2019

### Lightning Talks + Lunch

#### Ravikishore Kommajosyula, NSE: Model fitting using MATLAB

#### Naveen Arunachalam, ChemE: Democratizing Machine Learning: Convolutional Neural Networks in Pure Excel

#### Christopher Vincent Rackauckas, Mathematics: Neural Jump Diffusions and Neural Partial Differential Equations

#### Ali Ramadhan, EAPS: More accurate climate predictions with Oceananigans.jl

#### Sicong Huang, Brain+Cog: TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer

#### Dongchan Lee, MechE: Convex Restriction

#### Corbin Foucart, MechE: Webscraping airline price data with Python

## Jun 12, 2019

### MIT SIAM Coffee Hour + Social

## May 15

### Advancing the theory and applications of Lagrangian Coherent Structures methods for oceanic surface flows

#### Margaux Filippi, MIT/WHOI

## Apr 17, 2019

### PAC-Bayes Tree: Weighted Subtrees with Guarantees

#### Tinh Danh Nguyen, EECS

## Mar 28, 2019

### MIT SIAM Coffee Hour + Social

## Mar 21, 2019

### Distinguished Seminar: Mechanics & Inverse-design of Shape-shifting Structures

#### Speaker: Prof. Wim van Rees, MechE

## Mar 14, 2019

### Seminar: Transform & Learn: From nonlinear PDEs to low-dimensional polynomial models

#### Elizabeth Qian, AeroAstro

## Jan, 2019

### IAP Course: Practical Computer Science for Computational Scientists

#### Ravikishore Kommajosyula, NSE + Ricardo Baptista, AeroAstro

## Nov 28, 2018

### Seminar: Teaching a Neural Network Physics to Help Design Complex Devices

#### Sam Raymond, CEE

### Seminar: Reducing the Error Bars on Climate Predictions

#### Ali Ramadhan, EAPS

## Nov 15, 2018

### MIT SIAM Coffee Hour + Social

## Oct 25, 2018

### Seminar: Semiconductor Parameter Extraction (and more!) with Bayesian Inference

#### Rachel Kurchin, Materials Science and Engineering

## Sep 19, 2018

### MIT SIAM / ACSES Joint Coffee Hour

## Aug 9, 2018

### Distinguished Speaker Seminar: Big Data vs. Big Computation

#### Prof. Qiqi Wang, AeroAstro

## Jul 30, 2018

### Seminar: Iterated Pressure-Correction Projection Methods for the Unsteady Incompressible Navier-Stokes Equations

#### Jing Lin, MechE

## May 29, 2018

### Lightning Talks + Lunch

## Apr 17, 2018

### Distinguished Speaker Seminar: Intro to Julia

#### Prof. Alan Edelman, Mathematics

Ocean surface transport is at the core of many environmental disasters, including the spread of plastic pollution in seafood, the Deepwater Horizon oil spill and the nuclear contamination from the Fukushima Daiichi plant. Understanding and predicting flow transport, however, remains a scientific challenge, because it operates on multiple length- and time-scales that are set by the underlying dynamics. Building on the recent emergence of Lagrangian methods, this talk investigates the present-day abilities to describe and understand the organization of flow transport at the ocean surface, including the abilities to detect the underlying key structures, the regions of stirring and regions of coherence within the flow.

We present a weighted-majority classification approach over subtrees of a fixed tree, which provably achieves excess-risk of the same order as the best tree-pruning. Furthermore, the computational efficiency of pruning is maintained at both training and testing time despite having to aggregate over an exponential number of subtrees. We believe this is the first subtree aggregation approach with such guarantees.

Recent progress in additive manufacturing and materials engineering has led to a surge of interest in shape changing plate and shell like structures. Such structures are typically printed in a planar configuration and, when exposed to an ambient stimulus such as heat or humidity, morph into a desired three-dimensional shape. Viewed through the lens of differential geometry and elasticity, the application of the physical stimulus can be understood as a local change in the metric of a two dimensional surface embedded in three dimensions. In this talk I'll provide a short geometric and mechanic background to this problem, and demonstrate a numerical approach for simulating the elastic response to such a metric change for thin structures. I'll show my theoretical contributions on the inverse design of shape shifting bilayers, and discuss how these developments have led to the design and experimental realization of a 4D printed lattice that can undergo complex shape changes.

This talk presents Transform & Learn, a physics-based approach to learning efficient, low-dimensional models for large-scale nonlinear systems. The proposed method starts from the physics of the problem—in the form of governing partial differential equations (PDEs)—and introduces variable transformations to arrive at a state representation in which the system admits a quadratic formulation. The system can then be parametrized by matrix operators, which can be learned from data. To make the problem computationally tractable, transformed state data are projected onto a low-dimensional global basis. The dense, low-dimensional quadratic model is then learned via a least-squares model learning procedure. We demonstrate our method on model problems in fluid dynamics and combustion applications.

The interdisciplinary nature of computational research brings in members from a variety of backgrounds in math, science, and engineering. Practical knowledge of computer science is a major factor in conducting numerical research. In this course, we present some tools, techniques, and unwritten guidelines in computer science. The following topics will be covered, with customized content to fit a computational research audience, combined with some hands-on examples: (i) writing good code, (ii) debugging and maintaining code, (iii) collaboration and modern version control, (iv) data science, and (v) improving code efficiency. Held Tuesdays and Thursdays.

Starting with the first computational weather forecasts, a ridiculously crazy idea a hundred years ago, we'll see how modern climate models work and why uncertainties in climate predictions are so high despite their sophistication. Then I'll talk about how we're trying to reduce uncertainties in climate predictions by developing a new climate model in Julia that runs on massively parallel GPU accelerators and learns from observations and high-resolution simulations.

Starting with the first computational weather forecasts, a ridiculously crazy idea a hundred years ago, we'll see how modern climate models work and why uncertainties in climate predictions are so high despite their sophistication. Then I'll talk about how we're trying to reduce uncertainties in climate predictions by developing a new climate model in Julia that runs on massively parallel GPU accelerators and learns from observations and high-resolution simulations.

Bayesian parameter estimation is a widely-used approach for model optimization in a variety of fields including astrophysics, high-energy physics, and bioinformatics. However, it has not been adopted extensively for electronic device characterization. We have developed a generalized open-source Python code, Bayesim, that accepts sets of observed data as a function of experimental conditions and modeled data as a function of those same conditions as well as a set of parameters to be fit, and outputs a probability distribution over these parameters, accounting for both experimental and model uncertainty. Because models of electronic devices are frequently computationally expensive, we adopt a deterministic and adaptive scheme for sampling the parameter space and computing model uncertainty. I will discuss applications of the code in fundamental characterization of photovoltaic materials as well as current and planned future features, and leave plenty time for discussion of how Bayesim might be useful for your application!

What is the future of computing? Some believe it's big data. For others, it's big computation. Supporters of big data believe that most problems can be solved by gathering huge amounts of data and applying machine learning. Those who believe in big computation, however, postulates that all phenomena in the world can be explained by solving simple physical equations with sufficient computational power. So, who is right?

In solving the unsteady incompressible Navier-Stokes equations, typical pressure-correction schemes perform only one iteration per stage or time step, and suffer from splitting errors that result in spurious numerical boundary layers and a limited order of convergence in time. In this talk, we will show that performing iterations not only reduces the effects of the splitting errors, but can also be more efficient computationally than merely reducing the time step. This iteration takes the form of the Richardson iteration applied to the pressure-Schur complement problem. Our analysis also reveals the significant role played by the rotational correction in projection methods. Moreover, we have devised stopping criteria to recover the desired order of temporal convergence, and to drive the splitting error below the time-integration error. We have also developed and implemented the iterated pressure corrections with both multi-step and multi-stage time integration schemes. Our theoretical results are validated and illustrated by numerical test cases for the Stokes and Navier-Stokes equations, using implicit-explicit (IMEX) backwards differences and Runge-Kutta time-integration solvers. It is found that iterated pressure-correction schemes can retrieve the accuracy and temporal convergence order of fully-coupled schemes and are computationally more efficient than classic pressure-correction schemes. Finally, we will draw connections between pressure-correction schemes and classic SIMPLE-based schemes for incompressible flows, which were rarely compared to each other in the literature.

What makes a programming language “fast”? Alan Edelman, Professor of Applied Mathematics, gave an introduction to the Julia programming language. Julia is a high-level, high-performance dynamic programming language for numerical computing. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library.

Officers

Arkopal Dutt

Corbin Foucart

Mohammad Islam

President

Vice President

Treasurer

Nisha Chandramoorthy

Saviz Mowlavi

Rohit Supekar

Event Coordinator

Event Coordinator

Event Coordinator

Faculty Advisors

Dr. Jeremy Kepner

Prof. Alan Edelman

Lauren Milechin

MIT Lincoln Lab

MIT Mathematics

MIT EAPS

Prof. Gilbert Strang

Prof. Laurent Demanet

MIT Mathematics

MIT Mathematics