A processor-implemented method for generating a computational model to predict and optimize defibrillation mechanism of a Wearable Cardiac Defibrillator, comprises: obtaining imaging scan data associated with subject's torso, the imaging scan data indicative of subject's torso cardiac geometry, via one or more hardware processors; simulating a plurality of biophysical models of the WCD via the one or more hardware processors, each biophysical model of the plurality of biophysical models comprising a distinct configuration of a set of electrodes, wherein the set of electrodes are capable of measuring shock voltages from myocardium of the subject's torso generated in response to defibrillation using the imaging scan data; modeling, for each of the plurality of biophysical models, myocardial potential gradient by using a Finite Element method via the one or more hardware processors, wherein the myocardial potential gradient is indicative of an effect of voltages applied at the set of electrodes during defibrillation; computing, for each of the plurality of biophysical models, a probabilistic distribution of myocardial voltage gradient after defibrillation by combining a first exponential functions rising in amplitude for below a predetermined voltage gradient and a second exponential function decaying in amplitude for above the predetermined voltage gradient via the one or more hardware processors; calculating, for each of the plurality of the biophysical models, a divergence in the distribution of the myocardial potential gradient with respect to the probabilistic distribution of the myocardial voltage gradient via the one or more hardware processors; and selecting, via the one or more hardware processors, a biophysical model from amongst the plurality of biophysical models based on the calculation of the divergence. obtaining imaging scan data associated with subject's torso, the imaging scan data indicative of subject's torso cardiac geometry, via one or more hardware processors; simulating a plurality of biophysical models of the WCD via the one or more hardware processors, each biophysical model of the plurality of biophysical models comprising a distinct configuration of a set of electrodes, wherein the set of electrodes are capable of measuring shock voltages from myocardium of the subject's torso generated in response to defibrillation using the imaging scan data; modeling, for each of the plurality of biophysical models, myocardial potential gradient by using a Finite Element method via the one or more hardware processors, wherein the myocardial potential gradient is indicative of an effect of voltages applied at the set of electrodes during defibrillation; computing, for each of the plurality of biophysical models, a probabilistic distribution of myocardial voltage gradient after defibrillation by combining a first exponential functions rising in amplitude for below a predetermined voltage gradient and a second exponential function decaying in amplitude for above the predetermined voltage gradient via the one or more hardware processors; calculating, for each of the plurality of the biophysical models, a divergence in the distribution of the myocardial potential gradient with respect to the probabilistic distribution of the myocardial voltage gradient via the one or more hardware processors; and selecting, via the one or more hardware processors, a biophysical model from amongst the plurality of biophysical models based on the calculation of the divergence.