finished implementing thesis feedback

thesis/references.bib

@@ -269,7 +269,6 @@ Publisher: Multidisciplinary Digital Publishing Institute},
 	author = {Michalakes, John and Vachharajani, Manish},
 	urldate = {2025-02-14},
 	date = {2008-04},
-	note = {{ISSN}: 1530-2075},
 	keywords = {Acceleration, Bandwidth, Computer architecture, Concurrent computing, Graphics, Large-scale systems, Parallel processing, Predictive models, Weather forecasting, Yarn},
 	file = {Full Text PDF:C\:\\Users\\danwi\\Zotero\\storage\\ZFEVRLEZ\\Michalakes und Vachharajani - 2008 - GPU acceleration of numerical weather prediction.pdf:application/pdf;IEEE Xplore Abstract Record:C\:\\Users\\danwi\\Zotero\\storage\\PYY4F7JB\\4536351.html:text/html},
 }
@@ -321,7 +320,6 @@ Publisher: Multidisciplinary Digital Publishing Institute},
 	author = {Brunton, Steven L. and Proctor, Joshua L. and Kutz, J. Nathan},
 	urldate = {2025-02-26},
 	date = {2016-04-12},
-	note = {Publisher: Proceedings of the National Academy of Sciences},
 	file = {Full Text PDF:C\:\\Users\\danwi\\Zotero\\storage\\6R643NFZ\\Brunton et al. - 2016 - Discovering governing equations from data by sparse identification of nonlinear dynamical systems.pdf:application/pdf},
 }
 
@@ -423,7 +421,6 @@ Publisher: Multidisciplinary Digital Publishing Institute},
 	author = {Bartlett, Deaglan J. and Desmond, Harry and Ferreira, Pedro G.},
 	urldate = {2025-02-28},
 	date = {2024-08},
-	note = {Conference Name: {IEEE} Transactions on Evolutionary Computation},
 	keywords = {Optimization, Complexity theory, Mathematical models, Biological system modeling, Cosmology data analysis, minimum description length, model selection, Numerical models, Search problems, Standards, symbolic regression ({SR})},
 	file = {Eingereichte Version:C\:\\Users\\danwi\\Zotero\\storage\\Y6LFWDH2\\Bartlett et al. - 2024 - Exhaustive Symbolic Regression.pdf:application/pdf;IEEE Xplore Abstract Record:C\:\\Users\\danwi\\Zotero\\storage\\2HU5A8RL\\10136815.html:text/html},
 }
@@ -455,27 +452,10 @@ Publisher: Multidisciplinary Digital Publishing Institute},
 	author = {Huang, Qihang and Huang, Zhiyi and Werstein, Paul and Purvis, Martin},
 	urldate = {2025-03-01},
 	date = {2008-12},
-	note = {{ISSN}: 2379-5352},
 	keywords = {Computer architecture, Application software, Central Processing Unit, Computer graphics, Distributed computing, Grid computing, Multicore processing, Pipelines, Programming profession, Rendering (computer graphics)},
 	file = {IEEE Xplore Abstract Record:C\:\\Users\\danwi\\Zotero\\storage\\2FJP9K25\\references.html:text/html},
 }
 
-@article{han_hicuda_2011,
-	title = {{hiCUDA}: High-Level {GPGPU} Programming},
-	volume = {22},
-	url = {https://ieeexplore.ieee.org/abstract/document/5445082},
-	shorttitle = {{hiCUDA}},
-	abstract = {Graphics Processing Units ({GPUs}) have become a competitive accelerator for applications outside the graphics domain, mainly driven by the improvements in {GPU} programmability. Although the Compute Unified Device Architecture ({CUDA}) is a simple C-like interface for programming {NVIDIA} {GPUs}, porting applications to {CUDA} remains a challenge to average programmers. In particular, {CUDA} places on the programmer the burden of packaging {GPU} code in separate functions, of explicitly managing data transfer between the host and {GPU} memories, and of manually optimizing the utilization of the {GPU} memory. Practical experience shows that the programmer needs to make significant code changes, often tedious and error-prone, before getting an optimized program. We have designed {hiCUDA}},
-	pages = {78--90},
-	number = {1},
-	journaltitle = {{IEEE} Transactions on Parallel and Distributed Systems},
-	author = {Han, Tianyi David and Abdelrahman, Tarek S.},
-	urldate = {2025-03-01},
-	date = {2011},
-	note = {Conference Name: {IEEE} Transactions on Parallel and Distributed Systems},
-	file = {IEEE Xplore Abstract Record:C\:\\Users\\danwi\\Zotero\\storage\\5K63T7RB\\5445082.html:text/html},
-}
-
 @article{verbraeck_interactive_2021,
 	title = {Interactive Black-Hole Visualization},
 	volume = {27},
@@ -489,24 +469,10 @@ Publisher: Multidisciplinary Digital Publishing Institute},
 	author = {Verbraeck, Annemieke and Eisemann, Elmar},
 	urldate = {2025-03-02},
 	date = {2021-02},
-	note = {Conference Name: {IEEE} Transactions on Visualization and Computer Graphics},
 	keywords = {Rendering (computer graphics), Algorithms, Cameras, Computer Graphics Techniques, Distortion, Engineering, Mathematics, Observers, Physical \& Environmental Sciences, Ray tracing, Real-time systems, Visualization},
 	file = {PDF:C\:\\Users\\danwi\\Zotero\\storage\\HDASRGYN\\Verbraeck und Eisemann - 2021 - Interactive Black-Hole Visualization.pdf:application/pdf},
 }
 
-@book{hissbach_overview_2022,
-	title = {An Overview of Techniques for Egocentric Black Hole Visualization and Their Suitability for Planetarium Applications},
-	isbn = {978-3-03868-189-2},
-	url = {https://doi.org/10.2312/vmv.20221207},
-	abstract = {The visualization of black holes is used in science communication to educate people about our universe and concepts of general relativity. Recent visualizations aim to depict black holes in realtime, overcoming the challenge of efficient general relativistic ray tracing. In this state-of-the-art report, we provide the first overview of existing works about egocentric black hole visualization that generate images targeted at general audiences. We focus on Schwarzschild and Kerr black holes and discuss current methods to depict the distortion of background panoramas, point-shaped stars, nearby objects, and accretion disks. Approaches to realtime visualizations are highlighted. Furthermore, we present the implementation of a state-of-the-art black hole visualization in the planetarium software Uniview.},
-	publisher = {The Eurographics Association},
-	author = {Hissbach, Anny-Marleen and Dick, Christian and Lawonn, Kai},
-	urldate = {2025-03-02},
-	date = {2022},
-	langid = {english},
-	file = {Full Text PDF:C\:\\Users\\danwi\\Zotero\\storage\\TBBLEZ5N\\Hissbach et al. - 2022 - An Overview of Techniques for Egocentric Black Hole Visualization and Their Suitability for Planetar.pdf:application/pdf},
-}
-
 @inproceedings{schuurman_step-by-step_2013,
 	location = {New York, {NY}, {USA}},
 	title = {Step-by-step design and simulation of a simple {CPU} architecture},
@@ -537,7 +503,6 @@ Publisher: Multidisciplinary Digital Publishing Institute},
 	author = {Franchetti, F. and Kral, S. and Lorenz, J. and Ueberhuber, C.W.},
 	urldate = {2025-03-08},
 	date = {2005-02},
-	note = {Conference Name: Proceedings of the {IEEE}},
 	keywords = {Concurrent computing, Parallel processing, Automatic vectorization, Boosting, Computer aided instruction, Computer applications, Digital signal processing, digital signal processing ({DSP}), fast Fourier transform ({FFT}), Kernel, Registers, short vector single instruction, multiple data ({SIMD}), Signal processing algorithms, Spirals, symbolic vectorization},
 	file = {Eingereichte Version:C\:\\Users\\danwi\\Zotero\\storage\\J48HM9VD\\Franchetti et al. - 2005 - Efficient Utilization of SIMD Extensions.pdf:application/pdf;IEEE Xplore Abstract Record:C\:\\Users\\danwi\\Zotero\\storage\\W6PT75CV\\1386659.html:text/html},
 }
@@ -598,7 +563,6 @@ Publisher: Multidisciplinary Digital Publishing Institute},
 	institution = {{ENS} Lyon},
 	type = {Research Report},
 	author = {Collange, Caroline},
-	urldate = {2025-03-08},
 	date = {2011-09},
 	keywords = {{GPU}, {SIMD}, Control-flow reconvergence, {SIMT}},
 	file = {HAL PDF Full Text:C\:\\Users\\danwi\\Zotero\\storage\\M2WPWNXF\\Collange - 2011 - Stack-less SIMT reconvergence at low cost.pdf:application/pdf},
@@ -615,7 +579,6 @@ Publisher: Multidisciplinary Digital Publishing Institute},
 	author = {Fung, Wilson W. L. and Aamodt, Tor M.},
 	urldate = {2025-03-08},
 	date = {2011-02},
-	note = {{ISSN}: 2378-203X},
 	keywords = {Pipelines, Kernel, Graphics processing unit, Hardware, Instruction sets, Compaction, Random access memory},
 	file = {Full Text PDF:C\:\\Users\\danwi\\Zotero\\storage\\TRPWUTI6\\Fung und Aamodt - 2011 - Thread block compaction for efficient SIMT control flow.pdf:application/pdf;IEEE Xplore Abstract Record:C\:\\Users\\danwi\\Zotero\\storage\\LYPYEA8U\\5749714.html:text/html},
 }
@@ -635,19 +598,7 @@ Publisher: Multidisciplinary Digital Publishing Institute},
 	author = {Sutter, Herb},
 	urldate = {2025-03-13},
 	date = {2004-12},
-	file = {The Free Lunch Is Over\: A Fundamental Turn Toward Concurrency in Software:C\:\\Users\\danwi\\Zotero\\storage\\UU2CZWUR\\concurrency-ddj.html:text/html},
-}
-
-@article{bajwa_microsoft_2024,
-	title = {Microsoft, {OpenAI} plan \$100 billion data-center project, media report says},
-	url = {https://www.reuters.com/technology/microsoft-openai-planning-100-billion-data-center-project-information-reports-2024-03-29/},
-	abstract = {Microsoft and {OpenAI} are working on plans for a data center project that could cost as much as \$100 billion and include an artificial intelligence supercomputer called "Stargate" set to launch in 2028, The Information reported on Friday.},
-	journaltitle = {Reuters},
-	author = {Bajwa, Arsheeya},
-	urldate = {2025-03-13},
-	date = {2024-03-29},
-	langid = {english},
-	file = {Snapshot:C\:\\Users\\danwi\\Zotero\\storage\\G7PGNJJJ\\microsoft-openai-planning-100-billion-data-center-project-information-reports-2024-03-29.html:text/html},
+	file = {Free_Lunch.pdf:C\:\\Users\\danwi\\Zotero\\storage\\ICE8KXP8\\Free_Lunch.pdf:application/pdf;The Free Lunch Is Over\: A Fundamental Turn Toward Concurrency in Software:C\:\\Users\\danwi\\Zotero\\storage\\UU2CZWUR\\concurrency-ddj.html:text/html},
 }
 
 @article{koza_genetic_1994,
@@ -664,6 +615,7 @@ Publisher: Multidisciplinary Digital Publishing Institute},
 	urldate = {2025-03-13},
 	date = {1994-06},
 	langid = {english},
+	file = {PDF:C\:\\Users\\danwi\\Zotero\\storage\\SAHSU45G\\Koza - 1994 - Genetic programming as a means for programming computers by natural selection.pdf:application/pdf},
 }
 
 @article{koza_human-competitive_2010,
@@ -707,6 +659,158 @@ Publisher: Multidisciplinary Digital Publishing Institute},
 	author = {Sahoo, Subham S. and Lampert, Christoph H. and Martius, Georg},
 	urldate = {2025-03-13},
 	date = {2018},
-	note = {Version Number: 1},
-	keywords = {68T05, 68T30, 68T40, 62M20, 62J02, 65D15, 70E60, 93C40, {FOS}: Computer and information sciences, I.2.6; I.2.8, Machine Learning (cs.{LG}), Machine Learning (stat.{ML})},
+	keywords = {{FOS}: Computer and information sciences, I.2.6; I.2.8, Machine Learning (cs.{LG}), 68T05, 68T30, 68T40, 62M20, 62J02, 65D15, 70E60, 93C40, Machine Learning (stat.{ML})},
+}
+
+@article{han_hicuda_2011,
+	title = {{hiCUDA}: High-Level {GPGPU} Programming},
+	volume = {22},
+	rights = {https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/{IEEE}.html},
+	issn = {1045-9219},
+	url = {http://ieeexplore.ieee.org/document/5445082/},
+	doi = {10.1109/TPDS.2010.62},
+	shorttitle = {{hiCUDA}},
+	pages = {78--90},
+	number = {1},
+	journaltitle = {{IEEE} Transactions on Parallel and Distributed Systems},
+	shortjournal = {{IEEE} Trans. Parallel Distrib. Syst.},
+	author = {Han, Tianyi David and Abdelrahman, Tarek S.},
+	urldate = {2025-03-13},
+	date = {2011-01},
+	file = {PDF:C\:\\Users\\danwi\\Zotero\\storage\\PTANK4EC\\Han and Abdelrahman - 2011 - hiCUDA High-Level GPGPU Programming.pdf:application/pdf},
+}
+
+@article{brodtkorb_graphics_2013,
+	title = {Graphics processing unit ({GPU}) programming strategies and trends in {GPU} computing},
+	volume = {73},
+	rights = {https://www.elsevier.com/tdm/userlicense/1.0/},
+	issn = {07437315},
+	url = {https://linkinghub.elsevier.com/retrieve/pii/S0743731512000998},
+	doi = {10.1016/j.jpdc.2012.04.003},
+	pages = {4--13},
+	number = {1},
+	journaltitle = {Journal of Parallel and Distributed Computing},
+	shortjournal = {Journal of Parallel and Distributed Computing},
+	author = {Brodtkorb, André R. and Hagen, Trond R. and Sætra, Martin L.},
+	date = {2013-01},
+	langid = {english},
+	file = {Full Text:C\:\\Users\\danwi\\Zotero\\storage\\GZVCZUFG\\Brodtkorb et al. - 2013 - Graphics processing unit (GPU) programming strategies and trends in GPU computing.pdf:application/pdf},
+}
+
+@inproceedings{hissbach_overview_2022,
+	title = {An Overview of Techniques for Egocentric Black Hole Visualization and Their Suitability for Planetarium Applications},
+	isbn = {978-3-03868-189-2},
+	doi = {10.2312/vmv.20221207},
+	booktitle = {Vision, Modeling, and Visualization},
+	publisher = {The Eurographics Association},
+	author = {Hissbach, Anny-Marleen and Dick, Christian and Lawonn, Kai},
+	editor = {Bender, Jan and Botsch, Mario and Keim, Daniel A.},
+	date = {2022},
+	file = {Full Text PDF:C\:\\Users\\danwi\\Zotero\\storage\\TBBLEZ5N\\Hissbach et al. - 2022 - An Overview of Techniques for Egocentric Black Hole Visualization and Their Suitability for Planetar.pdf:application/pdf},
+}
+
+@inbook{guillemot_climate_2022,
+	edition = {1},
+	title = {Climate Models},
+	isbn = {978-1-009-08209-9 978-1-316-51427-6},
+	url = {https://www.cambridge.org/core/product/identifier/9781009082099%23CN-bp-14/type/book_part},
+	pages = {126--136},
+	booktitle = {A Critical Assessment of the Intergovernmental Panel on Climate Change},
+	publisher = {Cambridge University Press},
+	author = {Guillemot, Hélène},
+	bookauthor = {Hulme, Mike},
+	editor = {De Pryck, Kari},
+	urldate = {2025-03-14},
+	date = {2022-12-31},
+	file = {Full Text:C\:\\Users\\danwi\\Zotero\\storage\\MUKXXCV9\\Guillemot - 2022 - Climate Models.pdf:application/pdf},
+}
+
+@inproceedings{bomarito_bayesian_2022,
+	location = {Boston Massachusetts},
+	title = {Bayesian model selection for reducing bloat and overfitting in genetic programming for symbolic regression},
+	isbn = {978-1-4503-9268-6},
+	url = {https://dl.acm.org/doi/10.1145/3520304.3528899},
+	doi = {10.1145/3520304.3528899},
+	eventtitle = {{GECCO} '22: Genetic and Evolutionary Computation Conference},
+	pages = {526--529},
+	booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
+	publisher = {{ACM}},
+	author = {Bomarito, G. F. and Leser, P. E. and Strauss, N. C. M. and Garbrecht, K. M. and Hochhalter, J. D.},
+	urldate = {2025-03-14},
+	date = {2022-07-09},
+	langid = {english},
+	file = {PDF:C\:\\Users\\danwi\\Zotero\\storage\\ZPS5ZYYQ\\Bomarito et al. - 2022 - Bayesian model selection for reducing bloat and overfitting in genetic programming for symbolic regr.pdf:application/pdf},
+}
+
+@article{dabhi_survey_2012,
+	title = {A Survey on Techniques of Improving Generalization Ability of Genetic Programming Solutions},
+	rights = {{arXiv}.org perpetual, non-exclusive license},
+	url = {https://arxiv.org/abs/1211.1119},
+	doi = {10.48550/ARXIV.1211.1119},
+	abstract = {In the field of empirical modeling using Genetic Programming ({GP}), it is important to evolve solution with good generalization ability. Generalization ability of {GP} solutions get affected by two important issues: bloat and over-fitting. We surveyed and classified existing literature related to different techniques used by {GP} research community to deal with these issues. We also point out limitation of these techniques, if any. Moreover, the classification of different bloat control approaches and measures for bloat and over-fitting are also discussed. We believe that this work will be useful to {GP} practitioners in following ways: (i) to better understand concepts of generalization in {GP} (ii) comparing existing bloat and over-fitting control techniques and (iii) selecting appropriate approach to improve generalization ability of {GP} evolved solutions.},
+	author = {Dabhi, Vipul K. and Chaudhary, Sanjay},
+	urldate = {2025-03-14},
+	date = {2012},
+	keywords = {{FOS}: Computer and information sciences, Neural and Evolutionary Computing (cs.{NE})},
+	file = {PDF:C\:\\Users\\danwi\\Zotero\\storage\\JCULR888\\Dabhi and Chaudhary - 2012 - A Survey on Techniques of Improving Generalization Ability of Genetic Programming Solutions.pdf:application/pdf},
+}
+
+@book{kronberger_symbolic_2024,
+	title = {Symbolic Regression},
+	isbn = {978-1-315-16640-7},
+	url = {http://dx.doi.org/10.1201/9781315166407},
+	pagetotal = {308},
+	publisher = {Chapman and Hall/{CRC}},
+	author = {Kronberger, Gabriel and Burlacu, Bogdan and Kommenda, Michael and Winkler, Stephan M. and Affenzeller, Michael},
+	date = {2024-07},
+	file = {PDF:C\:\\Users\\danwi\\Zotero\\storage\\43RPG26H\\Kronberger et al. - 2024 - Symbolic Regression.pdf:application/pdf},
+}
+
+@misc{sun_symbolic_2023,
+	title = {Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search},
+	url = {http://arxiv.org/abs/2205.13134},
+	doi = {10.48550/arXiv.2205.13134},
+	shorttitle = {Symbolic Physics Learner},
+	abstract = {Nonlinear dynamics is ubiquitous in nature and commonly seen in various science and engineering disciplines. Distilling analytical expressions that govern nonlinear dynamics from limited data remains vital but challenging. To tackle this fundamental issue, we propose a novel Symbolic Physics Learner ({SPL}) machine to discover the mathematical structure of nonlinear dynamics. The key concept is to interpret mathematical operations and system state variables by computational rules and symbols, establish symbolic reasoning of mathematical formulas via expression trees, and employ a Monte Carlo tree search ({MCTS}) agent to explore optimal expression trees based on measurement data. The {MCTS} agent obtains an optimistic selection policy through the traversal of expression trees, featuring the one that maps to the arithmetic expression of underlying physics. Salient features of the proposed framework include search flexibility and enforcement of parsimony for discovered equations. The efficacy and superiority of the {SPL} machine are demonstrated by numerical examples, compared with state-of-the-art baselines.},
+	number = {{arXiv}:2205.13134},
+	publisher = {{arXiv}},
+	author = {Sun, Fangzheng and Liu, Yang and Wang, Jian-Xun and Sun, Hao},
+	urldate = {2025-03-14},
+	date = {2023-02-02},
+	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Symbolic Computation, Nonlinear Sciences - Chaotic Dynamics, Physics - Computational Physics},
+	file = {Preprint PDF:C\:\\Users\\danwi\\Zotero\\storage\\YBXYH5D6\\Sun et al. - 2023 - Symbolic Physics Learner Discovering governing equations via Monte Carlo tree search.pdf:application/pdf;Snapshot:C\:\\Users\\danwi\\Zotero\\storage\\D9SDYVT3\\2205.html:text/html},
+}
+
+@article{makke_interpretable_2024,
+	title = {Interpretable scientific discovery with symbolic regression: a review},
+	volume = {57},
+	issn = {1573-7462},
+	url = {https://doi.org/10.1007/s10462-023-10622-0},
+	doi = {10.1007/s10462-023-10622-0},
+	shorttitle = {Interpretable scientific discovery with symbolic regression},
+	abstract = {Symbolic regression is emerging as a promising machine learning method for learning succinct underlying interpretable mathematical expressions directly from data. Whereas it has been traditionally tackled with genetic programming, it has recently gained a growing interest in deep learning as a data-driven model discovery tool, achieving significant advances in various application domains ranging from fundamental to applied sciences. In this survey, we present a structured and comprehensive overview of symbolic regression methods, review the adoption of these methods for model discovery in various areas, and assess their effectiveness. We have also grouped state-of-the-art symbolic regression applications in a categorized manner in a living review.},
+	pages = {2},
+	number = {1},
+	journaltitle = {Artificial Intelligence Review},
+	shortjournal = {Artif Intell Rev},
+	author = {Makke, Nour and Chawla, Sanjay},
+	urldate = {2025-03-14},
+	date = {2024-01-02},
+	langid = {english},
+	keywords = {Artificial Intelligence, Automated Scientific Discovery, Interpretable {AI}, Symbolic Regression},
+	file = {Full Text PDF:C\:\\Users\\danwi\\Zotero\\storage\\7PFYYUJZ\\Makke and Chawla - 2024 - Interpretable scientific discovery with symbolic regression a review.pdf:application/pdf},
+}
+
+@misc{lemos_rediscovering_2022,
+	title = {Rediscovering orbital mechanics with machine learning},
+	url = {http://arxiv.org/abs/2202.02306},
+	doi = {10.48550/arXiv.2202.02306},
+	abstract = {We present an approach for using machine learning to automatically discover the governing equations and hidden properties of real physical systems from observations. We train a "graph neural network" to simulate the dynamics of our solar system's Sun, planets, and large moons from 30 years of trajectory data. We then use symbolic regression to discover an analytical expression for the force law implicitly learned by the neural network, which our results showed is equivalent to Newton's law of gravitation. The key assumptions that were required were translational and rotational equivariance, and Newton's second and third laws of motion. Our approach correctly discovered the form of the symbolic force law. Furthermore, our approach did not require any assumptions about the masses of planets and moons or physical constants. They, too, were accurately inferred through our methods. Though, of course, the classical law of gravitation has been known since Isaac Newton, our result serves as a validation that our method can discover unknown laws and hidden properties from observed data. More broadly this work represents a key step toward realizing the potential of machine learning for accelerating scientific discovery.},
+	number = {{arXiv}:2202.02306},
+	publisher = {{arXiv}},
+	author = {Lemos, Pablo and Jeffrey, Niall and Cranmer, Miles and Ho, Shirley and Battaglia, Peter},
+	urldate = {2025-03-14},
+	date = {2022-02-04},
+	keywords = {Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Machine Learning},
+	file = {Preprint PDF:C\:\\Users\\danwi\\Zotero\\storage\\9YPFHHRY\\Lemos et al. - 2022 - Rediscovering orbital mechanics with machine learning.pdf:application/pdf;Snapshot:C\:\\Users\\danwi\\Zotero\\storage\\YIFHYWCY\\2202.html:text/html},
 }