4,719 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
Machine learning in solar physics
The application of machine learning in solar physics has the potential to
greatly enhance our understanding of the complex processes that take place in
the atmosphere of the Sun. By using techniques such as deep learning, we are
now in the position to analyze large amounts of data from solar observations
and identify patterns and trends that may not have been apparent using
traditional methods. This can help us improve our understanding of explosive
events like solar flares, which can have a strong effect on the Earth
environment. Predicting hazardous events on Earth becomes crucial for our
technological society. Machine learning can also improve our understanding of
the inner workings of the sun itself by allowing us to go deeper into the data
and to propose more complex models to explain them. Additionally, the use of
machine learning can help to automate the analysis of solar data, reducing the
need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a
Living Review in Solar Physics (LRSP
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Unstable Periodic Orbits: a language to interpret the complexity of chaotic systems
Unstable periodic orbits (UPOs), exact periodic solutions of the evolution equation, offer a very
powerful framework for studying chaotic dynamical systems, as they allow one to dissect their
dynamical structure. UPOs can be considered the skeleton of chaotic dynamics, its essential
building blocks. In fact, it is possible to prove that in a chaotic system, UPOs are dense in
the attractor, meaning that it is always possible to find a UPO arbitrarily near any chaotic
trajectory. We can thus think of the chaotic trajectory as being approximated by different
UPOs as it evolves in time, jumping from one UPO to another as a result of their instability.
In this thesis we provide a contribution towards the use of UPOs as a tool to understand and
distill the dynamical structure of chaotic dynamical systems. We will focus on two models,
characterised by different properties, the Lorenz-63 and Lorenz-96 model.
The process of approximation of a chaotic trajectory in terms of UPOs will play a central role
in our investigation. In fact, we will use this tool to explore the properties of the attractor of
the system under the lens of its UPOs.
In the first part of the thesis we consider the Lorenz-63 model with the classic parameters’ value.
We investigate how a chaotic trajectory can be approximated using a complete set of UPOs
up to symbolic dynamics’ period 14. At each instant in time, we rank the UPOs according to
their proximity to the position of the orbit in the phase space. We study this process from
two different perspectives. First, we find that longer period UPOs overwhelmingly provide the
best local approximation to the trajectory. Second, we construct a finite-state Markov chain
by studying the scattering of the trajectory between the neighbourhood of the various UPOs.
Each UPO and its neighbourhood are taken as a possible state of the system. Through the
analysis of the subdominant eigenvectors of the corresponding stochastic matrix we provide a
different interpretation of the mixing processes occurring in the system by taking advantage of
the concept of quasi-invariant sets.
In the second part of the thesis we provide an extensive numerical investigation of the variability
of the dynamical properties across the attractor of the much studied Lorenz ’96 dynamical
system. By combining the Lyapunov analysis of the tangent space with the study of the
shadowing of the chaotic trajectory performed by a very large set of unstable periodic orbits,
we show that the observed variability in the number of unstable dimensions, which shows a
serious breakdown of hyperbolicity, is associated with the presence of a substantial number of
finite-time Lyapunov exponents that fluctuate about zero also when very long averaging times
are considered
Combining noisy well data and expert knowledge in a Bayesian calibration of a flow model under uncertainties: an application to solute transport in the Ticino basin
Groundwater flow modeling is commonly used to calculate groundwater heads,
estimate groundwater flow paths and travel times, and provide insights into
solute transport processes within an aquifer. However, the values of input
parameters that drive groundwater flow models are often highly uncertain due to
subsurface heterogeneity and geologic complexity in combination with lack of
measurements/unreliable measurements. This uncertainty affects the accuracy and
reliability of model outputs. Therefore, parameters' uncertainty must be
quantified before adopting the model as an engineering tool. In this study, we
model the uncertain parameters as random variables and use a Bayesian inversion
approach to obtain a posterior,data-informed, probability density function
(pdf) for them: in particular, the likelihood function we consider takes into
account both well measurements and our prior knowledge about the extent of the
springs in the domain under study. To keep the modelistic and computational
complexities under control, we assume Gaussianity of the posterior pdf of the
parameters. To corroborate this assumption, we run an identifiability analysis
of the model: we apply the inversion procedure to several sets of synthetic
data polluted by increasing levels of noise, and we determine at which levels
of noise we can effectively recover the "true value" of the parameters. We then
move to real well data (coming from the Ticino River basin, in northern Italy,
and spanning a month in summer 2014), and use the posterior pdf of the
parameters as a starting point to perform an Uncertainty Quantification
analysis on groundwater travel-time distributions.Comment: First submissio
Physics-Informed Deep Learning to Reduce the Bias in Joint Prediction of Nitrogen Oxides
Atmospheric nitrogen oxides (NOx) primarily from fuel combustion have
recognized acute and chronic health and environmental effects. Machine learning
(ML) methods have significantly enhanced our capacity to predict NOx
concentrations at ground-level with high spatiotemporal resolution but may
suffer from high estimation bias since they lack physical and chemical
knowledge about air pollution dynamics. Chemical transport models (CTMs)
leverage this knowledge; however, accurate predictions of ground-level
concentrations typically necessitate extensive post-calibration. Here, we
present a physics-informed deep learning framework that encodes
advection-diffusion mechanisms and fluid dynamics constraints to jointly
predict NO2 and NOx and reduce ML model bias by 21-42%. Our approach captures
fine-scale transport of NO2 and NOx, generates robust spatial extrapolation,
and provides explicit uncertainty estimation. The framework fuses
knowledge-driven physicochemical principles of CTMs with the predictive power
of ML for air quality exposure, health, and policy applications. Our approach
offers significant improvements over purely data-driven ML methods and has
unprecedented bias reduction in joint NO2 and NOx prediction
Complexity Science in Human Change
This reprint encompasses fourteen contributions that offer avenues towards a better understanding of complex systems in human behavior. The phenomena studied here are generally pattern formation processes that originate in social interaction and psychotherapy. Several accounts are also given of the coordination in body movements and in physiological, neuronal and linguistic processes. A common denominator of such pattern formation is that complexity and entropy of the respective systems become reduced spontaneously, which is the hallmark of self-organization. The various methodological approaches of how to model such processes are presented in some detail. Results from the various methods are systematically compared and discussed. Among these approaches are algorithms for the quantification of synchrony by cross-correlational statistics, surrogate control procedures, recurrence mapping and network models.This volume offers an informative and sophisticated resource for scholars of human change, and as well for students at advanced levels, from graduate to post-doctoral. The reprint is multidisciplinary in nature, binding together the fields of medicine, psychology, physics, and neuroscience
Structured machine learning models for robustness against different factors of variability in robot control
An important feature of human sensorimotor skill is our ability to learn to reuse them across different environmental contexts, in part due to our understanding of attributes of variability in these environments. This thesis explores how the structure of models used within learning for robot control could similarly help autonomous robots cope with variability, hence achieving skill generalisation. The overarching approach is to develop modular architectures that judiciously combine different forms of inductive bias for learning. In particular, we consider how models and policies should be structured in order to achieve robust behaviour in the face of different factors of variation - in the environment, in objects and in other internal parameters of a policy - with the end goal of more robust, accurate and data-efficient skill acquisition and adaptation.
At a high level, variability in skill is determined by variations in constraints presented by the external environment, and in task-specific perturbations that affect the specification of optimal action. A typical example of environmental perturbation would be variation in lighting and illumination, affecting the noise characteristics of perception. An example of task perturbations would be variation in object geometry, mass or friction, and in the specification of costs associated with speed or smoothness of execution. We counteract these factors of variation by exploring three forms of structuring: utilising separate data sets curated according to the relevant factor of variation, building neural network models that incorporate this factorisation into the very structure of the networks, and learning structured loss functions. The thesis is comprised of four projects exploring this theme within robotics planning and prediction tasks.
Firstly, in the setting of trajectory prediction in crowded scenes, we explore a modular architecture for learning static and dynamic environmental structure. We show that factorising the prediction problem from the individual representations allows for robust and label efficient forward modelling, and relaxes the need for full model re-training in new environments. This modularity explicitly allows for a more flexible and interpretable adaptation of trajectory prediction models to using
pre-trained state of the art models. We show that this results in more efficient motion prediction and allows for performance comparable to the state-of-the-art supervised 2D trajectory prediction.
Next, in the domain of contact-rich robotic manipulation, we consider a modular architecture that combines model-free learning from demonstration, in particular dynamic movement primitives (DMP), with modern model-free reinforcement learning (RL), using both on-policy and off-policy approaches. We show that factorising the skill learning problem to skill acquisition and error correction through policy adaptation strategies such as residual learning can help improve the overall performance of policies in the context of contact-rich manipulation. Our empirical evaluation demonstrates how to best do this with DMPs and propose “residual Learning from Demonstration“ (rLfD), a framework that combines DMPs with RL to learn a residual correction policy. Our evaluations, performed both in simulation and on a physical system, suggest that applying residual learning directly in task space and operating on the full pose of the robot can significantly improve the overall performance of DMPs. We show that rLfD offers a gentle to the joints solution that improves the task success and generalisation of DMPs. Last but not least, our study shows that the extracted correction policies can be transferred to different geometries and frictions through few-shot task adaptation.
Third, we employ meta learning to learn time-invariant reward functions, wherein both the objectives of a task (i.e., the reward functions) and the policy for performing that task optimally are learnt simultaneously. We propose a novel inverse reinforcement learning (IRL) formulation that allows us to 1) vary the length of execution by learning time-invariant costs, and 2) relax the temporal alignment requirements for learning from demonstration. We apply our method to two different types of cost formulations and evaluate their performance in the context of learning reward functions for simulated placement and peg in hole tasks executed on a 7DoF Kuka IIWA arm. Our results show that our approach enables learning temporally invariant rewards from misaligned demonstration that can also generalise spatially to out of distribution tasks.
Finally, we employ our observations to evaluate adversarial robustness in the context of transfer learning from a source trained on CIFAR 100 to a target network trained on CIFAR 10. Specifically, we study the effects of using robust optimisation in the source and target networks. This allows us to identify transfer learning strategies under which adversarial defences are successfully retained, in addition to revealing potential vulnerabilities. We study the extent to which adversarially robust features can preserve their defence properties against black and white-box attacks under three different transfer learning strategies. Our empirical evaluations give insights on how well adversarial robustness under transfer learning can generalise.
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