Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 23 for “"symbolic regression"”.
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MOBLLM: Model Building LLMs via Symbolic Regression and Experimental Design
… concurrently handle the experimental design and symbolic regression tasks for data obtained from 1) a black box 1D function and 2) a black box physical system. We propose further modifications to our base framework, and perform experiments to analyze how it performs under different experiment …
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Machine Learning for Physics: from Symbolic Regression to Quantum Simulation
… algorithms for efficient and parallelizable symbolic regression. Our methods demonstrate the potential for ML as a valuable tool for physics research.
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Advances in Symbolic Regression: From Generalized Formulation to Density Estimation and Inverse Problem
In this thesis, we explore the field of Symbolic Regression (SR), a middle ground between simple linear regression and complex inscrutable black box regressors such as neural networks. In essence, SR searches the space of mathematical expressions to find a model that best captures the relationship …
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The performance of coevolutionary topologies in developing competitive tree manipulation strategies for symbolic regression
… and simpler syntax and semantics. We utilize symbolic regression (SR) as a framework to analyze the error maximized by bugs and minimized by test. We adopt a hybrid evolutionary algorithm (EA) that implements the tree based phenotypic structure of genetic programming (GP) and the list-based …
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Novel Approaches to Discovery and Optimization in Physics: Symbolic Regression, Bayesian Optimization, and Topological Photonics
… propose a neural network-based method to perform symbolic regression and automatically learn the underlying equations from high-dimensional and complex datasets. The neural network-based model can integrate with other deep learning architectures, thus taking advantage of the powerful capabilities …
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Extensions to behavioral genetic programming
… programming and its application the task of symbolic regression. I introduce behavioral genetic programming [6] as an extension to genetic programming and explore various extensions to it. The codebase that I build is made sufficiently flexible to easily accommodate future adaptions to the …
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Evolvable Mathematical Models: A New Artificial Intelligence Paradigm
… trees which are evolved in a manner similar to Symbolic Regression in Genetic Programming. Equations are comprised of only the four basic mathematical operators, addition, subtraction, multiplication and division, as well as input and output variables and constants. From these operations, …
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Formulações semi-empíricas para o colapso hidrostático bi-simétrico de dutos flexíveis
… of a numerical model based on FEM through symbolic regression. Using this approach, expressions are proposed to obtain the response of the flexible pipe when subjected to crushing loads and to calculate the collapse pressure with dry and flooded annulus. The results showed an excellent …
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Mechanistic Interpretability for Progress Towards Quantitative AI Safety
… an RNN into a finite state machine and applying symbolic regression, MIPS successfully addresses 32 out of 62 algorithmic tasks, outperforming GPT-4 in 13 unique challenges. The work intends to take a step forward in enhancing the interpretability and reliability of AI systems, promising …
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Machine Learning based Predictive Modeling of Stochastic Systems
… model based on the soft computing technique of symbolic regression. The model offers high-level precise aerodynamic performance prediction through the blade element momentum process, making it a promising alternative for accurate and efficient stall delay correction in wind turbines.
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Flight And Symbolic Modeling Of A 3D Printed Ornithopter
… using a data-mining process based on the Eureqa symbolic regression software. The models developed by Eureqa were more accurate than analytical equations and were much simpler, as measured by equation size. Furthermore, Eureqa automatically separated the experimen- tal data into sums of distinct …
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Optimisation of multiplier-less FIR filter design techniques
… algorithms, has been evolved using techniques of symbolic regression and genetic programming. Although the evolved formula is very complex and not easily understandable, statistical analysis has shown that it produces more accurate results than traditional Kaiser's formula. In summary, several …
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Modeling virtualized application performance from hypervisor counters
… data in ensemble-based genetic programming for symbolic regression. This modeling technique is quite efficient at dealing with high-dimensional datasets, and it also generates interpretable models. After training models for web servers and virtual desktops, we test generalization across …
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Calibration of empirical, semi-empirical, and physics-based material models for the prediction of creep and tensile behaviour of Alloy 617
… calibrates twelve empirical models and applies symbolic regression (SR) to discover constitutive expressions for elevated-temperature creep behaviour. In general, the models were able to reasonably predict long-term creep behaviour at multiple temperatures using only short-term experimental …
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Combining artificial intelligence and robotic system in chemical product/process design
… nonlinear programming (MINLP) formulation for symbolic regression method was proposed for identification of physical models from noisy experimental data. The globally optimal search was extended to identify physical models and to cope with noise in the experimental data predictor variables.The …
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Discovery of trend dependencies over time-series
We improve constraint-based data quality using trend dependency (TD) discovery, extending existing order dependencies (ODs) to allow variations and exceptions. Unlike ODs, TDs capture approximate functional mappings between attributes, addressing the limitations of monotonicity. Our approach …
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Data-driven Dynamic Control Scheme for Antibody Producing CHO Cell Cultures in Fed-batch
… process efficiently was tested by constructing symbolic regression models. The performance of newly identified variables in predicting process behaviour was found superior to variable relationships found in literature. Following this, new models were generated using support vector machines (SVM) …
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Multiscale models based on statistical mechanics and physically-based machine learning for the thermo-hygro-mechanical behavior of spider-silk-like hierarchical materials
… at different scales. In particular, we employ a symbolic regression technique, known as 'Evolutionary Polynomial Regression', which integrates regression capabilities with the Genetic Programming paradigm, enabling the derivation of explicit analytical formulas, finally delivering a deeper …
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Last-Mile Logistics - Innovative Approaches to Network Optimization and Customer Service Offerings
… experiments and analyze our data using (symbolic) regression approaches. Our analyses suggest that the efficiency gains emerging from integrating first-mile pickup and last-mile delivery operations can be as high as 30%. However, the effective efficiency gains are highly sensitive to the …
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Lateral Torsional Buckling of Built-Up Beams
… develop dimensionless design equations through symbolic regression, characterizing elastic critical moments relative to the case of no interaction and deriving moment-gradient factors for common loading conditions and load-height coefficients. These proposed equations are integrated into a …
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