Kiri Karl Morgensternile, Hamburg

http://tartu.ester.ee/record=b1784645~S1*es

Explainable Artificial Intelligence for Mechanics: Physics-Explaining Neural Networks for Constitutive Models

(Artificial) neural networks have become increasingly popular in mechanics and materials sciences to accelerate computations with model order reduction techniques and as universal models for a wide variety of materials. However, the major disadvantage of neural networks remains: their numerous parameters are challenging to interpret and explain. Thus, neural networks are often labeled as black boxes, and their results often elude human interpretation. The new and active field of physics-informed neural networks attempts to mitigate this disadvantage by designing deep neural networks on the basis of mechanical knowledge. By using this a priori knowledge, deeper and more complex neural networks became feasible, since the mechanical assumptions can be explained. However, the internal reasoning and explanation of neural network parameters remain mysterious. Complementary to the physics-informed approach, we propose a first step towards a physics-explaining approach, which interprets neural networks trained on mechanical data a posteriori. This proof-of-concept explainable artificial intelligence approach aims at elucidating the black box of neural networks and their high-dimensional representations. Therein, the principal component analysis decorrelates the distributed representations in cell states of RNNs and allows the comparison to known and fundamental functions. The novel approach is supported by a systematic hyperparameter search strategy that identifies the best neural network architectures and training parameters. The findings of three case studies on fundamental constitutive models (hyperelasticity, elastoplasticity, and viscoelasticity) imply that the proposed strategy can help identify numerical and analytical closed-form solutions to characterize new materials

Kinetic modelling of methanol synthesis over commercial catalysts: A critical assessment

Kinetic modelling of methanol synthesis over commercial catalysts is of high importance for reactor and process design. Literature kinetic models were implemented and systematically discussed against a newly developed kinetic model based on published kinetic data. Deviations in the sensitivities of the kinetic models were explained by means of the experimentally covered parameter range. The simulation results proved that an extrapolation of the working range of the kinetic models can lead towards significant simulation errors especially with regard to pressure, stoichiometric number and CO/CO$_{2}$-ratio considerably limiting the applicability of kinetic models frequently applied in scientific literature. Therefore, the validated data range for kinetic models should be considered when detailed reactor simulations are carried out. With regard to Power-to-Methanol processes special attention should be drawn towards the rate limiting effect of water at high CO$_{2}$ contents in the syngas. Moreover, it was shown that kinetic models based on data measured over outdated catalysts show significantly lower activity than those derived from state-of-the-art catalysts and should therefore be applied with caution for reactor and process simulations. The plausible behavior of the herein proposed kinetic model was demonstrated by a systematic comparison towards established kinetic approaches within both, an ideal kinetic reactor and an industrial steam cooled tubular reactor. Relative to the state-of-the-art kinetic models it was proven that the herein proposed kinetic model can be applied over the complete industrially relevant working range for methanol synthesis

ΔFosB Regulates Gene Expression and Cognitive Dysfunction in a Mouse Model of Alzheimer\u27s Disease.

Alzheimer\u27s disease (AD) is characterized by cognitive decline and 5- to 10-fold increased seizure incidence. How seizures contribute to cognitive decline in AD or other disorders is unclear. We show that spontaneous seizures increase expression of ΔFosB, a highly stable Fos-family transcription factor, in the hippocampus of an AD mouse model. ΔFosB suppressed expression of the immediate early gene c-Fos, which is critical for plasticity and cognition, by binding its promoter and triggering histone deacetylation. Acute histone deacetylase (HDAC) inhibition or inhibition of ΔFosB activity restored c-Fos induction and improved cognition in AD mice. Administration of seizure-inducing agents to nontransgenic mice also resulted in ΔFosB-mediated suppression of c-Fos, suggesting that this mechanism is not confined to AD mice. These results explain observations that c-Fos expression increases after acute neuronal activity but decreases with chronic activity. Moreover, these results indicate a general mechanism by which seizures contribute to persistent cognitive deficits, even during seizure-free periods

Epigenetic suppression of hippocampal calbindin-D28k by ΔFosB drives seizure-related cognitive deficits.

The calcium-binding protein calbindin-D28k is critical for hippocampal function and cognition, but its expression is markedly decreased in various neurological disorders associated with epileptiform activity and seizures. In Alzheimer\u27s disease (AD) and epilepsy, both of which are accompanied by recurrent seizures, the severity of cognitive deficits reflects the degree of calbindin reduction in the hippocampal dentate gyrus (DG). However, despite the importance of calbindin in both neuronal physiology and pathology, the regulatory mechanisms that control its expression in the hippocampus are poorly understood. Here we report an epigenetic mechanism through which seizures chronically suppress hippocampal calbindin expression and impair cognition. We demonstrate that ΔFosB, a highly stable transcription factor, is induced in the hippocampus in mouse models of AD and seizures, in which it binds and triggers histone deacetylation at the promoter of the calbindin gene (Calb1) and downregulates Calb1 transcription. Notably, increasing DG calbindin levels, either by direct virus-mediated expression or inhibition of ΔFosB signaling, improves spatial memory in a mouse model of AD. Moreover, levels of ΔFosB and calbindin expression are inversely related in the DG of individuals with temporal lobe epilepsy (TLE) or AD and correlate with performance on the Mini-Mental State Examination (MMSE). We propose that chronic suppression of calbindin by ΔFosB is one mechanism through which intermittent seizures drive persistent cognitive deficits in conditions accompanied by recurrent seizures