The　accurate　non-invasive　detection　and　estimation　of　central　aortic　pressure　waveforms　(CAPW)　are　crucial　for　reliable　treatments　of　cardiovascular　system　diseases.　But　the　accuracy　and　practicality　of　current　estimation　methods　need　to　be　improved.　Our　study　combines　a　meta-learning　neural　network　and　a　physics-driven　method　to　accurately　estimate　CAPW　based　on　personalized　physiological　indicators.　We　collected　data　from　260　patients　who　underwent　catheterization　surgery,　using　measured　CAPW　and　personalized　physiological　indicators　(e.g.,　weight,　body　mass　index　(BMI),　radial　mean　arterial　pressure　(MAP),　heart　rate　(HR),　cardiac　output　(CO),　radial　systolic　blood　pressure　(SBP),　and　radial　diastolic　blood　pressure　(DBP))　as　input　for　neural　network　training.　The　output　of　the　neural　network　are　the　Gaussian　characteristic　parameters　of　the　single-period　decomposed　CAPW.　The　neural　network　model　was　constructed　using　the　model-agnostic　meta-learning　(MAML)　algorithm　framework.　Applying　the　physical　characteristics　of　CAPW　to　the　loss　function,　served　to　increase　the　constraints　on　the　output　and　improve　the　accuracy　of　CAPW　estimation.　To　verify　the　accuracy　of　the　model,　we　compared　measured　and　estimated　CAPW　in　52　patients.　The　results　are　consistent　with　a　normalized　root　mean　square　error　(NRMSE)　of　0.0206.　The　predictions　had　low　biases,　namely　SBP:　4.97　+/-　4.42　mmHg,　DBP:　4.78　+/-　5.98　mmHg,　and　MAP:　0.35　+/-　3.36　mmHg.　The　results　demonstrate　the　accuracy　and　practicability　of　the　approach　to　estimate　CAPW.　It　can　provide　personalized　parameters　to　calculate　myocardial　ischemia　indicators　(e.g.,　instantaneous　wave-free　ratio　[iFR]　and　fractional　flow　reserve　[FFR])　and　may　contribute　to　the　early　monitoring　and　prevention　of　cardiovascular　diseases.