publications
2024
- Active Modulation of Er3+ Emission Lifetime by VO2 Phase-Change Thin FilmsBoris Kalinic, Tiziana Cesca, Alessandro Lovo, Carlo Scian, Roberto Macaluso, and 4 more authorsAdvanced Photonics Research, Feb 2024
The active modulation of optical response of quantum emitters at the nanoscale is of paramount importance to realize tunable light sources for nanophotonic devices. Herein, a thin film of phase-change material (VO2) is coupled to a 20 nm-thick silica layer embedding Er3+ ions, and it is demonstrated how the active tuning of the local density of optical states near the erbium emitters provided by the thermally induced semiconductor-to-metal transition of VO2 can be used to dynamically control the Er3+ emission lifetime at telecom wavelength (1.54 μm). A decay rate contrast of a factor 2 is obtained between high temperature (90 ^∘C), when VO2 is metallic, and room temperature, when VO2 is semiconductor, in agreement with calculations performed with the classical dipole oscillator analytical model. A hysteretic behavior is observed by measuring the Er3+ lifetime as a function of the temperature, whose parameters are consistent with those of grazing incidence X-ray diffraction and optical transmittance measurements. The fractions of Er3+ ions that couple with VO2 in each phase at the different temperatures are determined by the analysis of the temporal decays. The results make the investigated system an optimal candidate for the development of tunable photon sources at telecom wavelength.
- The Role of Edge States for Early-Warning of Tipping PointsJohannes Lohmann, Alfred B. Hansen, Alessandro Lovo, Ruth Chapman, Freddy Bouchet, and 1 more authorOct 2024
Tipping points (TP) are often described as low-dimensional bifurcations, and are associated with early-warning signals (EWS) due to critical slowing down (CSD). CSD is an increase in amplitude and correlation of noise-induced fluctuations away from a reference attractor as the TP is approached. But for high-dimensional systems it is not obvious which variables or observables would display the critical dynamics and carry CSD. Many variables may display no CSD, or show changes in variability not related to a TP. It is thus helpful to identify beforehand which observables are relevant for a given TP. Here we propose this may be achieved by knowledge of an unstable edge state that separates the reference from an alternative attractor that remains after the TP. This is because stochastic fluctuations away from the reference attractor are preferentially directed towards the edge state along a most likely path (the instanton). As the TP is approached the edge state and reference attractor typically become closer, and the fluctuations can evolve further along the instanton. This can be exploited to find observables with substantial CSD, which we demonstrate using conceptual dynamical systems models and climate model simulations of a collapse of the Atlantic Meridional Overturning Circulation (AMOC).
- Tackling the Accuracy-Interpretability Trade-off in a Hierarchy of Machine Learning Models for the Prediction of Extreme HeatwavesAlessandro Lovo, Amaury Lancelin, Corentin Herbert, and Freddy BouchetOct 2024
When performing predictions that use Machine Learning (ML), we are mainly interested in performance and interpretability. This generates a natural trade-off, where complex models generally have higher skills but are harder to explain and thus trust. Interpretability is particularly important in the climate community, where we aim at gaining a physical understanding of the underlying phenomena. Even more so when the prediction concerns extreme weather events with high impact on society. In this paper, we perform probabilistic forecasts of extreme heatwaves over France, using a hierarchy of increasingly complex ML models, which allows us to find the best compromise between accuracy and interpretability. More precisely, we use models that range from a global Gaussian Approximation (GA) to deep Convolutional Neural Networks (CNNs), with the intermediate steps of a simple Intrinsically Interpretable Neural Network (IINN) and a model using the Scattering Transform (ScatNet). Our findings reveal that CNNs provide higher accuracy, but their black-box nature severely limits interpretability, even when using state-of-the-art Explainable Artificial Intelligence (XAI) tools. In contrast, ScatNet achieves similar performance to CNNs while providing greater transparency, identifying key scales and patterns in the data that drive predictions. This study underscores the potential of interpretability in ML models for climate science, demonstrating that simpler models can rival the performance of their more complex counterparts, all the while being much easier to understand. This gained interpretability is crucial for building trust in model predictions and uncovering new scientific insights, ultimately advancing our understanding and management of extreme weather events.
- Gaussian Framework and Optimal Projection of Weather Fields for Prediction of Extreme EventsMay 2024
Extreme events are the major weather related hazard for humanity. It is then of crucial importance to have a good understanding of their statistics and to be able to forecast them. However, lack of sufficient data makes their study particularly challenging. In this work we provide a simple framework to study extreme events that tackles the lack of data issue by using the whole dataset available, rather than focusing on the extremes in the dataset. To do so, we make the assumption that the set of predictors and the observable used to define the extreme event follow a jointly Gaussian distribution. This naturally gives the notion of an optimal projection of the predictors for forecasting the event. We take as a case study extreme heatwaves over France, and we test our method on an 8000-year-long intermediate complexity climate model time series and on the ERA5 reanalysis dataset. For a-posteriori statistics, we observe and motivate the fact that composite maps of very extreme events look similar to less extreme ones. For prediction, we show that our method is competitive with off-the-shelf neural networks on the long dataset and outperforms them on reanalysis. The optimal projection pattern, which makes our forecast intrinsically interpretable, highlights the importance of soil moisture deficit and quasi-stationary Rossby waves as precursors to extreme heatwaves.
- A Gaussian Framework for Optimal Prediction of Extreme Heat WavesMar 2024
- Studying Extreme Climate Events Using Machine Learning and Rare Event AlgorithmsAlessandro LovoEcole normale supérieure de lyon - ENS LYON, Oct 2024
Extreme events in weather and climate are among the most detrimental effects of the Climate Crisis. Extreme heatwaves, for instance, have been responsible for significant excess mortality. Moreover, as the climate warms, there is a risk, still unsatisfactorily quantified, that extreme events could make us cross Tipping Points in the Earth System, leading to abrupt changes in the current climate.It is thus of paramount importance to improve our understanding of such extreme events and our ability to forecast them. However, by their nature, extreme events are rare, so there are very few instances in observational data and simulating them with state-of-the-art climate models can be very expensive. To counter this lack of data issue, Rare Event Algorithms can be applied to significantly improve the efficiency in simulating extreme events. Such algorithms need an estimate of the probability of occurrence of the event conditioned on the state of the system, and this is exactly what the prediction task provides.This thesis develops in two main directions. The first is to use Machine Learning (ML) to estimate from long climate model simulations the probabilities of extreme heatwaves over France. In particular, through a hierarchy of increasingly complex ML models, the tradeoffs between amount of data, performance and interpretability of the predictions are investigated. The second is to apply a Rare Event Algorithm to the study of the abrupt collapse of the Atlantic Meridional Overturning Circulation (AMOC). Finally these two pieces are put together to investigate how coupling Machine Learning and Rare Event Algorithms may improve our ability to sample and predict rare events.
2023
- Interpretable Probabilistic Forecast of Extreme Heat WavesAlessandro Lovo, Corentin Herbert, and Freddy BouchetFeb 2023
2021
- Control of the Emission Properties of Quantum Emitters by Coupling with Phase-Change NanomaterialsAlessandro LovoSep 2021
In this work, the effect of the coupling between a thin film of the phase changing material vanadium dioxide (VO2) and a layer of Er3+ quantum emitters embedded in a silica matrix is investigated. Several characterization techniques of the VO2 thin films are carried out, revealing a direct correlation between the crystallite grain size and the width of the hysteresis loops. After a synthesis recipe by magnetron sputtering co-deposition and subsequent annealing which yields the finest grains is found, it is then used to fabricate samples with both a VO2 thin film and an Er:SiO2 emitting layer. Subsequently, the effects of the Metal-Insulator Transition of VO2 on the emission properties of the Er3+ ions are studied both experimentally and with the use of numerical simulations, showing an amplification of the radiative lifetime of the emitters by a factor of 7 for the insulating phase of VO2 and 14 for the metallic one and allowing the measurement of a full and detailed hysteresis cycle of the photoluminescent emission of the Er3+ ions.