Regressing the vector field of a dynamical system from a finite number of observed states is a natural way to learn surrogate models for such systems. We present variants of cross-validation (Kernel Flows [31] and its variants based on Maximum Mean Discrepancy and Lyapunov exponents) as simple approaches for learning the kernel used in these emulators
In chaotic dynamical systems such as the weather, prediction errors grow faster in some situations t...
Demand for learning, design and decision making is higher than ever before. Autonomous vehicles need...
The applicability of machine learning for predicting chaotic dynamics relies heavily upon the data u...
Regressing the vector field of a dynamical system from a finite number of observed states is a natur...
Regressing the vector field of a dynamical system from a finite number of observed states is a natur...
Transferring information from data to models is crucial to many scientific disciplines. Typically, t...
Model inference for dynamical systems aims to estimate the future behaviour of a system from observa...
We consider the problem of learning Stochastic Differential Equations of the form $dX_t = f(X_t)dt+\...
The problem of determining the underlying dynamics of a system when only given data of its state ove...
Dynamical systems have been used to describe a vast range of phenomena, including physical sciences...
Complex computational models are used nowadays in all fields of applied sciences to predict the beha...
This paper explores learning emulators for parameter estimation with uncertainty estimation of high-...
Empirically observed time series in physics, biology, or medicine, are commonly generated by some un...
Recent advances in computing algorithms and hardware have rekindled interest in developing high accu...
Abstract We present a novel kernel-based machine learning algorithm for identifying the low-dimens...
In chaotic dynamical systems such as the weather, prediction errors grow faster in some situations t...
Demand for learning, design and decision making is higher than ever before. Autonomous vehicles need...
The applicability of machine learning for predicting chaotic dynamics relies heavily upon the data u...
Regressing the vector field of a dynamical system from a finite number of observed states is a natur...
Regressing the vector field of a dynamical system from a finite number of observed states is a natur...
Transferring information from data to models is crucial to many scientific disciplines. Typically, t...
Model inference for dynamical systems aims to estimate the future behaviour of a system from observa...
We consider the problem of learning Stochastic Differential Equations of the form $dX_t = f(X_t)dt+\...
The problem of determining the underlying dynamics of a system when only given data of its state ove...
Dynamical systems have been used to describe a vast range of phenomena, including physical sciences...
Complex computational models are used nowadays in all fields of applied sciences to predict the beha...
This paper explores learning emulators for parameter estimation with uncertainty estimation of high-...
Empirically observed time series in physics, biology, or medicine, are commonly generated by some un...
Recent advances in computing algorithms and hardware have rekindled interest in developing high accu...
Abstract We present a novel kernel-based machine learning algorithm for identifying the low-dimens...
In chaotic dynamical systems such as the weather, prediction errors grow faster in some situations t...
Demand for learning, design and decision making is higher than ever before. Autonomous vehicles need...
The applicability of machine learning for predicting chaotic dynamics relies heavily upon the data u...
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