Romania and its NATO allies have reacted angrily after a Russian drone crashed into an apartment building in eastern Romania, injuring two people. The Ministry of Foreign Affairs in Bucharest on Friday labelled the crash of the drone, part of an overnight attack aimed at Ukraine, a serious violation of international law. The incident is just the latest incursion along the alliance's eastern flank, raising concern that the risk of an open confrontation between Russia and NATO states is rising. Romania said the overnight drone was tracked by radar in its airspace before crashing onto the roof of a residential building in the city of Galati. Two F-16 fighter jets and a helicopter were scrambled, as authorities issued emergency alerts to residents.

A Russian drone hit an apartment building in Romania, the country's defence ministry said early on Friday, causing a fire and injuring two people. The drone crashed in the eastern city of Galati as Russia carried out attacks in Ukraine near the border, the ministry said in a statement. The Romanian General Inspectorate for Emergency Situations said the drone's entire explosive payload detonated, causing a fire on the 10th floor of the residential building. Russian drones have strayed across the border of the Nato member country a number of times during the four-year war with Ukraine, but this was the first time citizens from Romania had been hurt. Russia has yet to comment on the incident. This incident represents a serious and irresponsible escalation on the part of the Russian Federation, Romania's foreign ministry said, adding Bucharest had informed the Nato secretary general and requested measures to accelerate the transfer of anti-drone capabilities to Romania.

For most process systems, knowledge of the model structure is incomplete. This missing physics must then be learned from experimental data. Recently, a combination of universal differential equations and symbolic regression has become a popular tool to discover these missing physics. Universal differential equations employ neural networks to represent missing parts of the model structure, and symbolic regression aims to make these neural networks interpretable. These machine learning techniques require high-quality data to successfully recover the true model structure. To gather such informative data, a sequential experimental design technique is developed which is based on optimally discriminating between the plausible model structures suggested by symbolic regression. This technique is then applied to discovering the missing physics of a bioreactor.

Model-based design of experiments (MBDOE) is essential for efficient parameter estimation in nonlinear dynamical systems. However, conventional adaptive MBDOE requires costly posterior inference and design optimization between each experimental step, precluding real-time applications. We address this by combining Deep Adaptive Design (DAD), which amortizes sequential design into a neural network policy trained offline, with differentiable mechanistic models. For dynamical systems with known governing equations but uncertain parameters, we extend sequential contrastive training objectives to handle nuisance parameters and propose a transformer-based policy architecture that respects the temporal structure of dynamical systems. We demonstrate the approach on four systems of increasing complexity: a fed-batch bioreactor with Monod kinetics, a Haldane bioreactor with uncertain substrate inhibition, a two-compartment pharmacokinetic model with nuisance clearance parameters, and a DC motor for real-time deployment.

It is not yet well understood how to integrate information in space and time into a single, general model.