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thesis/references.bib

@@ -628,3 +628,85 @@ Publisher: Multidisciplinary Digital Publishing Institute},
 	date = {2025-03},
 	file = {HIP programming model — HIP 6.3.42134 Documentation:C\:\\Users\\danwi\\Zotero\\storage\\6KRNU6PG\\programming_model.html:text/html},
 }
+
+@online{sutter_free_2004,
+	title = {The Free Lunch Is Over: A Fundamental Turn Toward Concurrency in Software},
+	url = {http://www.gotw.ca/publications/concurrency-ddj.htm},
+	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},
+}
+
+@article{koza_genetic_1994,
+	title = {Genetic programming as a means for programming computers by natural selection},
+	volume = {4},
+	rights = {http://www.springer.com/tdm},
+	issn = {0960-3174, 1573-1375},
+	url = {http://link.springer.com/10.1007/BF00175355},
+	doi = {10.1007/BF00175355},
+	number = {2},
+	journaltitle = {Statistics and Computing},
+	shortjournal = {Stat Comput},
+	author = {Koza, {JohnR}.},
+	urldate = {2025-03-13},
+	date = {1994-06},
+	langid = {english},
+}
+
+@article{koza_human-competitive_2010,
+	title = {Human-competitive results produced by genetic programming},
+	volume = {11},
+	issn = {1389-2576, 1573-7632},
+	url = {http://link.springer.com/10.1007/s10710-010-9112-3},
+	doi = {10.1007/s10710-010-9112-3},
+	pages = {251--284},
+	number = {3},
+	journaltitle = {Genetic Programming and Evolvable Machines},
+	shortjournal = {Genet Program Evolvable Mach},
+	author = {Koza, John R.},
+	urldate = {2025-03-13},
+	date = {2010-09},
+	langid = {english},
+	file = {Full Text:C\:\\Users\\danwi\\Zotero\\storage\\Y32QERP5\\Koza - 2010 - Human-competitive results produced by genetic programming.pdf:application/pdf},
+}
+
+@misc{martius_extrapolation_2016,
+	title = {Extrapolation and learning equations},
+	rights = {{arXiv}.org perpetual, non-exclusive license},
+	url = {https://arxiv.org/abs/1610.02995},
+	doi = {10.48550/ARXIV.1610.02995},
+	abstract = {In classical machine learning, regression is treated as a black box process of identifying a suitable function from a hypothesis set without attempting to gain insight into the mechanism connecting inputs and outputs. In the natural sciences, however, finding an interpretable function for a phenomenon is the prime goal as it allows to understand and generalize results. This paper proposes a novel type of function learning network, called equation learner ({EQL}), that can learn analytical expressions and is able to extrapolate to unseen domains. It is implemented as an end-to-end differentiable feed-forward network and allows for efficient gradient based training. Due to sparsity regularization concise interpretable expressions can be obtained. Often the true underlying source expression is identified.},
+	publisher = {{arXiv}},
+	author = {Martius, Georg and Lampert, Christoph H.},
+	urldate = {2025-03-13},
+	date = {2016},
+	note = {Version Number: 1},
+	keywords = {68T05, 68T30, 68T40, 62J02, 65D15, Artificial Intelligence (cs.{AI}), {FOS}: Computer and information sciences, I.2.6; I.2.8, Machine Learning (cs.{LG})},
+}
+
+@misc{sahoo_learning_2018,
+	title = {Learning Equations for Extrapolation and Control},
+	rights = {{arXiv}.org perpetual, non-exclusive license},
+	url = {https://arxiv.org/abs/1806.07259},
+	doi = {10.48550/ARXIV.1806.07259},
+	abstract = {We present an approach to identify concise equations from data using a shallow neural network approach. In contrast to ordinary black-box regression, this approach allows understanding functional relations and generalizing them from observed data to unseen parts of the parameter space. We show how to extend the class of learnable equations for a recently proposed equation learning network to include divisions, and we improve the learning and model selection strategy to be useful for challenging real-world data. For systems governed by analytical expressions, our method can in many cases identify the true underlying equation and extrapolate to unseen domains. We demonstrate its effectiveness by experiments on a cart-pendulum system, where only 2 random rollouts are required to learn the forward dynamics and successfully achieve the swing-up task.},
+	publisher = {{arXiv}},
+	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})},
+}