Scholarly Works per year
Scholarly Works per year
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Dive into the research topics where Mathematics is active. These topic labels come from the works of this organization's members. Together they form a unique fingerprint.
Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
Profiles
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p-adic theory for partial toric exponential sums
Haessig, C. D., Jan 2027, In: Finite Fields and their Applications. 117, 102863.Research output: Contribution to journal › Article › peer-review
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Real-time stochastic assessment of dynamic N-1 grid contingencies
Almada, A., Pagnier, L., Goldshtein, I., Kazi, S. R. & Chertkov, M., Jan 2027, In: Electric Power Systems Research. 262, 113614.Research output: Contribution to journal › Article › peer-review
Open Access -
Adaptive Path Integral Diffusion: AdaPID
Chertkov, M. & Behjoo, H., May 2026, In: Entropy. 28, 5, 492.Research output: Contribution to journal › Article › peer-review
Open Access
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Low-dose risk bounds via simultaneous confidence bands
Piegorsch, W. W. (CoPI)
Project: Research project
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Model-independent benchmark dose estimation for quantitative risk assessment
Piegorsch, W. W. (PI)
Project: Research project
Datasets
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A pool-adjacent-violators-algorithm approach to detect infinite parameter estimates in one-regressor dose–response models with asymptotes
Deutsch, R. C. (Creator), Habing, B. (Creator) & Piegorsch, W. W. (Creator), figshare, 2014
DOI: 10.6084/m9.figshare.1145718, https://figshare.com/articles/A_pool_adjacent_violators_algorithm_approach_to_detect_infinite_parameter_estimates_in_one_regressor_dose_8211_response_models_with_asymptotes/1145718
Dataset
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Optimal parameter selection in Weeks’ method for numerical Laplace transform inversion based on machine learning
Kano, P. O. (Creator), Brio, M. (Contributor) & Bailey, J. (Creator), SAGE Journals, 2021
DOI: 10.25384/sage.c.5357908, https://sage.figshare.com/collections/Optimal_parameter_selection_in_Weeks_method_for_numerical_Laplace_transform_inversion_based_on_machine_learning/5357908
Dataset
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Optimal parameter selection in Weeks’ method for numerical Laplace transform inversion based on machine learning
Kano, P. O. (Creator), Brio, M. (Contributor) & Bailey, J. (Creator), SAGE Journals, 2021
DOI: 10.25384/sage.c.5357908.v1, https://sage.figshare.com/collections/Optimal_parameter_selection_in_Weeks_method_for_numerical_Laplace_transform_inversion_based_on_machine_learning/5357908/1
Dataset