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Analysis of forest disturbance patterns in relation to precipitation seasonality is important for understanding African tropical forest dynamics under changing climate conditions and different levels of human activities. Newly available radar-based forest disturbance information now enables an investigation of the intra-annual relationship between precipitation and forest disturbance in a spatially and temporally explicit manner, especially in the tropics, where frequent cloud cover hinders the use of optical-based remote sensing products. In this study, we applied cross-correlation on monthly precipitation and forest disturbance time series for 2019 and 2020 at a 0.5° grid in the African rainforest. We used the magnitude of the correlation and time lag to assess the intra-annual relationship between precipitation and forest disturbance, and introduced accessibility proxies to analyse the spatial variation of the relationship. Results revealed that a significant negative correlation between forest disturbance and precipitation dominates the study region. We found that significant negative correlations appear on average closer to settlements with overall smaller variations in travel time to settlements compared to grid cells with non-significant and significant positive correlation. The magnitude of the negative correlation increases as the travel time to settlements increases, implying that forest disturbances in less accessible areas are more affected by precipitation seasonality and that in particular human-induced disturbance activities are predominantly carried out in the drier months. Few areas showed a significant positive correlation, mainly resulting from natural causes such as flooding. These new insights in the interaction between forest disturbance, precipitation and accessibility provide a step forward in understanding the complex interactions that underlie the complexity of forest loss patterns that we can increasingly capture with Earth Observation approaches. As such, they can support forest conservation and management in coping with climate change induced changes of precipitation patterns in African rainforest countries.

LAMMPS is a powerful simulator originally developed for molecular dynamics that, today, also accounts for other particle-based algorithms such as DEM, SPH, or Peridynamics. The versatility of this software is further enhanced by the fact that it is open-source and modifiable by users. This property suits particularly well Discrete Multiphysics and hybrid models that combine multiple particle methods in the same simulation. Modifying LAMMPS can be challenging for researchers with little coding experience. The available material explaining how to modify LAMMPS is either too basic or too advanced for the average researcher. In this work, we provide several examples, with increasing level of complexity, suitable for researchers and practitioners in physics and engineering, who are familiar with coding without been experts. For each feature, step by step instructions for implementing them in LAMMPS are shown to allow researchers to easily follow the procedure and compile a new version of the code. The aim is to fill a gap in the literature with particular reference to the scientific community that uses particle methods for (discrete) multiphysics.

LAMMPS is a powerful simulator originally developed for molecular dynamics that, today, also accounts for other particle-based algorithms such as DEM, SPH, or Peridynamics. The versatility of this software is further enhanced by the fact that it is open-source and modifiable by users. This property suits particularly well Discrete Multiphysics and hybrid models that combine multiple particle methods in the same simulation. Modifying LAMMPS can be challenging for researchers with little coding experience. The available material explaining how to modify LAMMPS is either too basic or too advanced for the average researcher. In this work, we provide several examples, with increasing level of complexity, suitable for researchers and practitioners in physics and engineering, who are familiar with coding without been experts. For each feature, step by step instructions for implementing them in LAMMPS are shown to allow researchers to easily follow the procedure and compile a new version of the code. The aim is to fill a gap in the literature with particular reference to the scientific community that uses particle methods for (discrete) multiphysics. [ABSTRACT FROM AUTHOR]

We study the optimal design of numerical integrators for dissipative systems, for which there exists an underlying thermodynamic structure known as GENERIC (general equation for the nonequilibrium reversible-irreversible coupling). We present a framework to construct structure-preserving integrators by splitting the system into reversible and irreversible dynamics. The reversible part, which is often degenerate and reduces to a Hamiltonian form on its symplectic leaves, is solved by using a symplectic method (e.g. Verlet) with degenerate variables being left unchanged, for which an associated modified Hamiltonian (and subsequently a modified energy) in the form of a series expansion can be obtained by using backward error analysis. The modified energy is then used to construct a modified friction matrix associated with the irreversible part in such a way that a modified degeneracy condition is satisfied. The modified irreversible dynamics can be further solved by an explicit midpoint method if not exactly solvable. Our findings are verified by various numerical experiments, demonstrating the superiority of structure-preserving integrators over alternative schemes in terms of not only the accuracy control of both energy conservation and entropy production but also the preservation of the conformal symplectic structure in the case of linearly damped systems. [ABSTRACT FROM AUTHOR]

We study numerical methods for sampling probability measures in high dimension where the underlying model is only approximately identified with a gradient system. Extended stochastic dynamical methods are discussed which have application to multiscale models, nonequilibrium molecular dynamics, and Bayesian sampling techniques arising in emerging machine learning applications. In addition to providing a more comprehensive discussion of the foundations of these methods, we propose a new numerical method for the adaptive Langevin/stochastic gradient Nosé- Hoover thermostat that achieves a dramatic improvement in numerical efficiency over the most popular stochastic gradient methods reported in the literature. We also demonstrate that the newly established method inherits a superconvergence property (fourth order convergence to the invariant measure for configurational quantities) recently demonstrated in the setting of Langevin dynamics. Our findings are verified by numerical experiments. [ABSTRACT FROM AUTHOR]