Jure Brence
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View article: Predictions of satellite retrieval failures of air quality using machine learning
Predictions of satellite retrieval failures of air quality using machine learning Open
The growing fleet of Earth Observation (EO) satellites is capturing unprecedented quantities of information about the concentration and distribution of trace gases in the Earth's atmosphere. Depending on the instrument and algorithm, the y…
View article: Probabilistic grammars for modeling dynamical systems from coarse, noisy, and partial data
Probabilistic grammars for modeling dynamical systems from coarse, noisy, and partial data Open
Ordinary differential equations (ODEs) are a widely used formalism for the mathematical modeling of dynamical systems, a task omnipresent in scientific domains. The paper introduces a novel method for inferring ODEs from data, which extend…
View article: Dynobench - extended Strogatz benchmark for system identification methods
Dynobench - extended Strogatz benchmark for system identification methods Open
The dynobench repository contains a benchmark for system identification methods. Currently includes models of 10 dynamical systems: Bacterial respiration, Bar magnets, Glider, Lotka-Volterra, Predator-Prey, Shearflow and Van der Pol from t…
View article: Dynobench - extended Strogatz benchmark for system identification methods
Dynobench - extended Strogatz benchmark for system identification methods Open
The dynobench repository contains a benchmark for system identification methods. Currently includes models of 10 dynamical systems: Bacterial respiration, Bar magnets, Glider, Lotka-Volterra, Predator-Prey, Shearflow and Van der Pol from t…
View article: Probabilistic grammars for modeling dynamical systems from coarse, noisy, and partial data
Probabilistic grammars for modeling dynamical systems from coarse, noisy, and partial data Open
Ordinary differential equations (ODEs) are a widely used formalism for the mathematical modeling of dynamical systems, a task omnipresent in scientific domains. The paper introduces a novel method for inferring ODEs from data. It extends P…
View article: Dimensionally-consistent equation discovery through probabilistic attribute grammars
Dimensionally-consistent equation discovery through probabilistic attribute grammars Open
Equation discovery, also known as symbolic regression, is a machine learning task of inducing closed-form equations from data and background knowledge. The latter takes various forms. Domain-specific knowledge can constrain the space of ca…
View article: Boosting the performance of quantum annealers using machine learning
Boosting the performance of quantum annealers using machine learning Open
Noisy intermediate-scale quantum (NISQ) devices are spearheading the second quantum revolution. Of these, quantum annealers are the only ones currently offering real world, commercial applications on as many as 5000 qubits. The size of pro…
View article: Surrogate models of radiative transfer codes for atmospheric trace gas retrievals from satellite observations
Surrogate models of radiative transfer codes for atmospheric trace gas retrievals from satellite observations Open
Inversion of radiative transfer models (RTMs) is key to interpreting satellite observations of air quality and greenhouse gases, but is computationally expensive. Surrogate models that emulate the full forward physical RTM can speed up the…
View article: Exploring machine learning methods for absolute configuration determination with vibrational circular dichroism
Exploring machine learning methods for absolute configuration determination with vibrational circular dichroism Open
Database of the VCD spectra used in the article `Exploring machine learning methods for absolute configuration determination with vibrational circular dichroism'.
View article: Probabilistic grammars for equation discovery
Probabilistic grammars for equation discovery Open
Equation discovery, also known as symbolic regression, is a type of automated\nmodeling that discovers scientific laws, expressed in the form of equations,\nfrom observed data and expert knowledge. Deterministic grammars, such as\ncontext-…
View article: Exploring machine learning methods for absolute configuration determination with vibrational circular dichroism
Exploring machine learning methods for absolute configuration determination with vibrational circular dichroism Open
The capabilities of machine learning models to extract the absolute configuration of a series of compounds from their vibrational circular dichroism spectra have been demonstrated. The important spectral areas are identified.
View article: Cesium bright matter-wave solitons and soliton trains
Cesium bright matter-wave solitons and soliton trains Open
A study of bright matter-wave solitons of a cesium Bose-Einstein condensate (BEC) is presented. Production of a single soliton is demonstrated and dependence of soliton atom number on the interatomic interaction is investigated. Formation …