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pro vyhledávání: '"Mark, K"'
Parameter identifiability refers to the capability of accurately inferring the parameter values of a model from its observations (data). Traditional analysis methods exploit analytical properties of the closed form model, in particular sensitivity an
Externí odkaz:
http://arxiv.org/abs/2412.18663
Autor:
Kurniawan, Yonatan, Neilsen, Tracianne B., Francis, Benjamin L., Stankovic, Alex M., Wen, Mingjian, Nikiforov, Ilia, Tadmor, Ellad B., Bulatov, Vasily V., Lordi, Vincenzo, Transtrum, Mark K.
The efficacy of mathematical models heavily depends on the quality of the training data, yet collecting sufficient data is often expensive and challenging. Many modeling applications require inferring parameters only as a means to predict other quant
Externí odkaz:
http://arxiv.org/abs/2411.02740
Autor:
Harbick, Aiden V., Transtrum, Mark K.
Modern superconducting radio frequency (SRF) applications require precise control of a wide range of material properties, from microscopic material parameters to mesoscopic/macroscopic surface structures. Mesoscopic simulation of superconductors has
Externí odkaz:
http://arxiv.org/abs/2410.20078
A central problem in data science is to use potentially noisy samples of an unknown function to predict function values for unseen inputs. In classical statistics, the predictive error is understood as a trade-off between the bias and the variance th
Externí odkaz:
http://arxiv.org/abs/2408.08294
Modern reinforcement learning has been conditioned by at least three dogmas. The first is the environment spotlight, which refers to our tendency to focus on modeling environments rather than agents. The second is our treatment of learning as finding
Externí odkaz:
http://arxiv.org/abs/2407.10583