35 research outputs found
2023- The Twenty-seventh Annual Symposium of Student Scholars
The full program book from the Twenty-seventh Annual Symposium of Student Scholars, held on April 18-21, 2023. Includes abstracts from the presentations and posters.https://digitalcommons.kennesaw.edu/sssprograms/1027/thumbnail.jp
Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics
Three recent breakthroughs due to AI in arts and science serve as motivation:
An award winning digital image, protein folding, fast matrix multiplication.
Many recent developments in artificial neural networks, particularly deep
learning (DL), applied and relevant to computational mechanics (solid, fluids,
finite-element technology) are reviewed in detail. Both hybrid and pure machine
learning (ML) methods are discussed. Hybrid methods combine traditional PDE
discretizations with ML methods either (1) to help model complex nonlinear
constitutive relations, (2) to nonlinearly reduce the model order for efficient
simulation (turbulence), or (3) to accelerate the simulation by predicting
certain components in the traditional integration methods. Here, methods (1)
and (2) relied on Long-Short-Term Memory (LSTM) architecture, with method (3)
relying on convolutional neural networks. Pure ML methods to solve (nonlinear)
PDEs are represented by Physics-Informed Neural network (PINN) methods, which
could be combined with attention mechanism to address discontinuous solutions.
Both LSTM and attention architectures, together with modern and generalized
classic optimizers to include stochasticity for DL networks, are extensively
reviewed. Kernel machines, including Gaussian processes, are provided to
sufficient depth for more advanced works such as shallow networks with infinite
width. Not only addressing experts, readers are assumed familiar with
computational mechanics, but not with DL, whose concepts and applications are
built up from the basics, aiming at bringing first-time learners quickly to the
forefront of research. History and limitations of AI are recounted and
discussed, with particular attention at pointing out misstatements or
misconceptions of the classics, even in well-known references. Positioning and
pointing control of a large-deformable beam is given as an example.Comment: 275 pages, 158 figures. Appeared online on 2023.03.01 at
CMES-Computer Modeling in Engineering & Science
Roadmap on Electronic Structure Codes in the Exascale Era
Electronic structure calculations have been instrumental in providing many important insights into a range of physical and chemical properties of various molecular and solid-state systems. Their importance to various fields, including materials science, chemical sciences, computational chemistry and device physics, is underscored by the large fraction of available public supercomputing resources devoted to these calculations. As we enter the exascale era, exciting new opportunities to increase simulation numbers, sizes, and accuracies present themselves. In order to realize these promises, the community of electronic structure software developers will however first have to tackle a number of challenges pertaining to the efficient use of new architectures that will rely heavily on massive parallelism and hardware accelerators. This roadmap provides a broad overview of the state-of-the-art in electronic structure calculations and of the various new directions being pursued by the community. It covers 14 electronic structure codes, presenting their current status, their development priorities over the next five years, and their plans towards tackling the challenges and leveraging the opportunities presented by the advent of exascale computing
Combining computer simulations and deep learning to understand and predict protein structural dynamics
Molecular dynamics simulations provide a means to characterize the ensemble of structures that a protein adopts in solution. These structural ensembles provide crucial information about how proteins function, and these ensembles also reveal potential drug binding sites that are not observable from static protein structures (i.e. cryptic pockets). However, analyzing these high- dimensional datasets to understand protein function remains challenging. Additionally, finding cryptic pockets using simulation data is slow and expensive, which makes the appeal of computationally screening for cryptic pockets limited to a narrow set of circumstances. In this thesis, I develop deep learning based methods to overcome these challenges. First, I develop a deep learning algorithm, called DiffNets, to deal with the high-dimensionality of structural ensembles. DiffNets takes structural ensembles from similar systems with different biochemical properties and learns to highlight structural features that distinguish the systems, ultimately connecting structural signatures to their associated biochemical properties. Using DiffNets, I provide structural insights that explain how naturally occurring genetic variants of the oxytocin receptor alter signaling. Additionally, DiffNets help reveal how a SARS-CoV-2 protein involved in immune evasion becomes activated. Next, I use MD simulations to hunt for cryptic pockets across the SARS-CoV-2 proteome, which led to the discovery of more than 50 new potential druggable sites. Because this effort required an extraordinary amount of resources, I developed a deep learning approach to predict sites of cryptic pockets from single protein structures. This approach reduces the time to identify if a protein has a cryptic pocket by ~10,000-fold compared to the next best method
Roadmap on Electronic Structure Codes in the Exascale Era
Electronic structure calculations have been instrumental in providing many
important insights into a range of physical and chemical properties of various
molecular and solid-state systems. Their importance to various fields,
including materials science, chemical sciences, computational chemistry and
device physics, is underscored by the large fraction of available public
supercomputing resources devoted to these calculations. As we enter the
exascale era, exciting new opportunities to increase simulation numbers, sizes,
and accuracies present themselves. In order to realize these promises, the
community of electronic structure software developers will however first have
to tackle a number of challenges pertaining to the efficient use of new
architectures that will rely heavily on massive parallelism and hardware
accelerators. This roadmap provides a broad overview of the state-of-the-art in
electronic structure calculations and of the various new directions being
pursued by the community. It covers 14 electronic structure codes, presenting
their current status, their development priorities over the next five years,
and their plans towards tackling the challenges and leveraging the
opportunities presented by the advent of exascale computing.Comment: Submitted as a roadmap article to Modelling and Simulation in
Materials Science and Engineering; Address any correspondence to Vikram
Gavini ([email protected]) and Danny Perez ([email protected]
Path Integral Quantum Monte Carlo Method for Light Nuclei
I describe the first continuous space nuclear path integral quantum Monte
Carlo method, and calculate the ground state properties of light nuclei
including Deuteron, Triton, Helium-3 and Helium-4, using both local chiral
interaction up to next-to-next-to-leading-order and the Argonne
interaction. Compared with diffusion based quantum Monte Carlo methods such as
Green's function Monte Carlo and auxiliary field diffusion Monte Carlo, path
integral quantum Monte Carlo has the advantage that it can directly calculate
the expectation value of operators without tradeoff, whether they commute with
the Hamiltonian or not. For operators that commute with the Hamiltonian, e.g.,
the Hamiltonian itself, the path integral quantum Monte Carlo light-nuclei
results agree with Green's function Monte Carlo and auxiliary field diffusion
Monte Carlo results. For other operator expectations which are important to
understand nuclear measurements but do not commute with the Hamiltonian and
therefore cannot be accurately calculated by diffusion based quantum Monte
Carlo methods without tradeoff, the path integral quantum Monte Carlo method
gives reliable results. I show root-mean-square radii, one-particle number
density distributions, and Euclidean response functions for single-nucleon
couplings. I also systematically describe all the sampling algorithms used in
this work, the strategies to make the computation efficient, the error
estimations, and the details of the implementation of the code to perform
calculations. This work can serve as a benchmark test for future calculations
of larger nuclei or finite temperature nuclear matter using path integral
quantum Monte Carlo.Comment: Rong Chen's 2020 PhD thesis at Arizona State University, Tempe,
Arizona, USA. PhD Advisor, Professor Kevin E. Schmid
Artificial Intelligence in the Capitalist University
Using Marxist critique, this book explores manifestations of Artificial Intelligence (AI) in Higher Education and demonstrates how it contributes to the functioning and existence of the capitalist university. Challenging the idea that AI is a break from previous capitalist technologies, the book offers nuanced examination of the impacts of AI on the control and regulation of academic work and labour, on digital learning and remote teaching, and on the value of learning and knowledge. Applying a Marxist perspective, Preston argues that commodity fetishism, surveillance, and increasing productivity ushered in by the growth of AI, further alienates and exploits academic labour and commodifies learning and research. The text puts forward a solid theoretical framework and methodology for thinking about AI to inform critical and revolutionary pedagogies. Offering an impactful and timely analysis, this book provides a critical engagement and application of key Marxist concepts in the study of AI’s role in Higher Education. It will be of interest to those working or researching in Higher Education
Artificial Intelligence in the Capitalist University Academic Labour, Commodification, and Value
Using Marxist critique, this book explores manifestations of Artificial Intelligence (AI) in Higher Education and demonstrates how it contributes to the functioning and existence of the capitalist university.
Challenging the idea that AI is a break from previous capitalist technologies, the book offers nuanced examination of the impacts of AI on the control and regulation of academic work and labour, on digital learning and remote teaching, and on the value of learning and knowledge. Applying a Marxist perspective, Preston argues that commodity fetishism, surveillance, and increasing productivity ushered in by the growth of AI, further alienates and exploits academic labour and commodifies learning and research. The text puts forward a solid theoretical framework and methodology for thinking about AI to inform critical and revolutionary pedagogies.
Offering an impactful and timely analysis, this book provides a critical engagement and application of key Marxist concepts in the study of AI’s role in Higher Education. It will be of interest to those working or researching in Higher Education