No Arabic abstract
The 20th century has revealed two important limitations of scientific knowledge. On the one hand, the combination of Poincares nonlinear dynamics and Heisenbergs uncertainty principle leads to a world picture where physical reality is, in many respects, intrinsically undetermined. On the other hand, Godels incompleteness theorems reveal us the existence of mathematical truths that cannot be demonstrated. More recently, Chaitin has proved that, from the incompleteness theorems, it follows that the random character of a given mathematical sequence cannot be proved in general (it is undecidable). I reflect here on the consequences derived from the indeterminacy of the future and the undecidability of randomness, concluding that the question of the presence or absence of finality in nature is fundamentally outside the scope of the scientific method.
We explore the different meanings of quantum uncertainty contained in Heisenbergs seminal paper from 1927, and also some of the precise definitions that were explored later. We recount the controversy about Anschaulichkeit, visualizability of the theory, which Heisenberg claims to resolve. Moreover, we consider Heisenbergs programme of operational analysis of concepts, in which he sees himself as following Einstein. Heisenbergs work is marked by the tensions between semiclassical arguments and the emerging modern quantum theory, between intuition and rigour, and between shaky arguments and overarching claims. Nevertheless, the main message can be taken into the new quantum theory, and can be brought into the form of general theorems. They come in two kinds, not distinguished by Heisenberg. These are, on one hand, constraints on preparations, like the usual textbook uncertainty relation, and, on the other, constraints on joint measurability, including trade-offs between accuracy and disturbance.
Accurate estimation of parameters is paramount in developing high-fidelity models for complex dynamical systems. Model-based optimal experiment design (OED) approaches enable systematic design of dynamic experiments to generate input-output data sets with high information content for parameter estimation. Standard OED approaches however face two challenges: (i) experiment design under incomplete system information due to unknown true parameters, which usually requires many iterations of OED; (ii) incapability of systematically accounting for the inherent uncertainties of complex systems, which can lead to diminished effectiveness of the designed optimal excitation signal as well as violation of system constraints. This paper presents a robust OED approach for nonlinear systems with arbitrarily-shaped time-invariant probabilistic uncertainties. Polynomial chaos is used for efficient uncertainty propagation. The distinct feature of the robust OED approach is the inclusion of chance constraints to ensure constraint satisfaction in a stochastic setting. The presented approach is demonstrated by optimal experimental design for the JAK-STAT5 signaling pathway that regulates various cellular processes in a biological cell.
We propose two new methods to calibrate the parameters of the Epidemic-Type Aftershock Sequence (ETAS) model based on expectation maximization (EM) while accounting for temporal variation of catalog completeness. The first method allows for model calibration on earthquake catalogs with long history, featuring temporal variation of the magnitude of completeness, $m_c$. This extended calibration technique is beneficial for long-term Probabilistic Seismic Hazard Assessment (PSHA), which is often based on a mixture of instrumental and historical catalogs. The second method jointly estimates ETAS parameters and high-frequency detection incompleteness to address the potential biases in parameter calibration due to short-term aftershock incompleteness. For this, we generalize the concept of completeness magnitude and consider a rate- and magnitude-dependent detection probability $-$ embracing incompleteness instead of avoiding it. Using synthetic tests, we show that both methods can accurately invert the parameters of simulated catalogs. We then use them to estimate ETAS parameters for California using the earthquake catalog since 1932. To explore how the newly gained information from the second method affects earthquakes predictability, we conduct pseudo-prospective forecasting experiments for California. Our proposed model significantly outperforms the base ETAS model, and we find that the ability to include small earthquakes for simulation of future scenarios is the main driver of the improvement. Our results point towards a preference of earthquakes to trigger similarly sized aftershocks, which has potentially major implications for our understanding of earthquake interaction mechanisms and for the future of seismicity forecasting.
Due to the edges position between the cloud and the users, and the recent surge of deep neural network (DNN) applications, edge computing brings about uncertainties that must be understood separately. Particularly, the edge users locally specific requirements that change depending on time and location cause a phenomenon called dataset shift, defined as the difference between the training and test datasets representations. It renders many of the state-of-the-art approaches for resolving uncertainty insufficient. Instead of finding ways around it, we exploit such phenomenon by utilizing a new principle: AI model diversity, which is achieved when the user is allowed to opportunistically choose from multiple AI models. To utilize AI model diversity, we propose Model Diversity Network (MoDNet), and provide design guidelines and future directions for efficient learning driven communication schemes.
Aggregation of heating, ventilation, and air conditioning (HVAC) loads can provide reserves to absorb volatile renewable energy, especially solar photo-voltaic (PV) generation. However, the time-varying PV generation is not perfectly known when the system operator decides the HVAC control schedules. To consider the unknown uncertain PV generation, in this paper, we formulate a distributionally robust chance-constrained (DRCC) building load control problem under two typical ambiguity sets: the moment-based and Wasserstein ambiguity sets. We derive mixed-integer linear programming (MILP) reformulations for DRCC problems under both sets. Especially for the DRCC problem under the Wasserstein ambiguity set, we utilize the right-hand side (RHS) uncertainty to derive a more compact MILP reformulation than the commonly known MILP reformulations with big-M constants. All the results also apply to general individual chance constraints with RHS uncertainty. Furthermore, we propose an adjustable chance-constrained variant to achieve a trade-off between the operational risk and costs. We derive MILP reformulations under the Wasserstein ambiguity set and second-order conic programming (SOCP) reformulations under the moment-based set. Using real-world data, we conduct computational studies to demonstrate the efficiency of the solution approaches and the effectiveness of the solutions.