cpu
p0_max:  0.3981804193255513
save epoch: 0 , loss: tensor(0.3034) , ic: tensor(0.3032) , res: tensor(3.1409e-05) , res grad: tensor(9.0985e-06)
... RAR mean res:  tensor(0.0046)
... RES add [x,t]: tensor([[109.8470]]) tensor([[1.6497]]) . max res value:  tensor([0.0099])
save epoch: 2 , loss: tensor(0.2755) , ic: tensor(0.2744) , res: tensor(0.0002) , res grad: tensor(4.0831e-05)
save epoch: 3 , loss: tensor(0.2527) , ic: tensor(0.2501) , res: tensor(0.0004) , res grad: tensor(7.5673e-05)
save epoch: 4 , loss: tensor(0.2240) , ic: tensor(0.2187) , res: tensor(0.0009) , res grad: tensor(0.0001)
save epoch: 5 , loss: tensor(0.1936) , ic: tensor(0.1835) , res: tensor(0.0018) , res grad: tensor(0.0002)
save epoch: 6 , loss: tensor(0.1743) , ic: tensor(0.1570) , res: tensor(0.0033) , res grad: tensor(0.0002)
save epoch: 11 , loss: tensor(0.1568) , ic: tensor(0.1552) , res: tensor(0.0003) , res grad: tensor(5.3460e-05)
... RAR mean res:  tensor(0.0025)
... RES add [x,t]: tensor([[90.0019]]) tensor([[2.6904]]) . max res value:  tensor([0.0065])
save epoch: 488 , loss: tensor(0.1489) , ic: tensor(0.1489) , res: tensor(1.7166e-06) , res grad: tensor(5.5970e-06)
... RAR mean res:  tensor(0.0012)
... RES add [x,t]: tensor([[109.5389]]) tensor([[1.0032]]) . max res value:  tensor([0.0120])
save epoch: 504 , loss: tensor(0.1410) , ic: tensor(0.1408) , res: tensor(5.2034e-06) , res grad: tensor(2.6298e-05)
save epoch: 510 , loss: tensor(0.1339) , ic: tensor(0.1336) , res: tensor(1.8283e-05) , res grad: tensor(4.6038e-05)
save epoch: 514 , loss: tensor(0.1267) , ic: tensor(0.1262) , res: tensor(3.8637e-05) , res grad: tensor(6.5143e-05)
save epoch: 517 , loss: tensor(0.1195) , ic: tensor(0.1188) , res: tensor(6.1997e-05) , res grad: tensor(8.2676e-05)
save epoch: 520 , loss: tensor(0.1108) , ic: tensor(0.1098) , res: tensor(9.4336e-05) , res grad: tensor(0.0001)
save epoch: 522 , loss: tensor(0.1041) , ic: tensor(0.1029) , res: tensor(0.0001) , res grad: tensor(0.0001)
save epoch: 524 , loss: tensor(0.0970) , ic: tensor(0.0955) , res: tensor(0.0002) , res grad: tensor(0.0001)
save epoch: 526 , loss: tensor(0.0896) , ic: tensor(0.0880) , res: tensor(0.0002) , res grad: tensor(0.0002)
save epoch: 530 , loss: tensor(0.0804) , ic: tensor(0.0773) , res: tensor(0.0005) , res grad: tensor(0.0002)
save epoch: 534 , loss: tensor(0.0762) , ic: tensor(0.0713) , res: tensor(0.0007) , res grad: tensor(0.0003)
save epoch: 536 , loss: tensor(0.0695) , ic: tensor(0.0661) , res: tensor(0.0004) , res grad: tensor(0.0003)
save epoch: 540 , loss: tensor(0.0632) , ic: tensor(0.0610) , res: tensor(0.0003) , res grad: tensor(0.0002)
save epoch: 542 , loss: tensor(0.0597) , ic: tensor(0.0575) , res: tensor(0.0002) , res grad: tensor(0.0002)
save epoch: 546 , loss: tensor(0.0557) , ic: tensor(0.0533) , res: tensor(0.0003) , res grad: tensor(0.0002)
save epoch: 551 , loss: tensor(0.0523) , ic: tensor(0.0500) , res: tensor(0.0003) , res grad: tensor(0.0002)
save epoch: 552 , loss: tensor(0.0491) , ic: tensor(0.0469) , res: tensor(0.0002) , res grad: tensor(0.0002)
save epoch: 559 , loss: tensor(0.0456) , ic: tensor(0.0427) , res: tensor(0.0003) , res grad: tensor(0.0003)
save epoch: 560 , loss: tensor(0.0432) , ic: tensor(0.0409) , res: tensor(0.0003) , res grad: tensor(0.0002)
save epoch: 566 , loss: tensor(0.0395) , ic: tensor(0.0364) , res: tensor(0.0003) , res grad: tensor(0.0003)
save epoch: 568 , loss: tensor(0.0373) , ic: tensor(0.0349) , res: tensor(0.0003) , res grad: tensor(0.0002)
save epoch: 575 , loss: tensor(0.0337) , ic: tensor(0.0310) , res: tensor(0.0003) , res grad: tensor(0.0003)
save epoch: 578 , loss: tensor(0.0320) , ic: tensor(0.0293) , res: tensor(0.0003) , res grad: tensor(0.0003)
save epoch: 587 , loss: tensor(0.0287) , ic: tensor(0.0256) , res: tensor(0.0003) , res grad: tensor(0.0003)
save epoch: 591 , loss: tensor(0.0267) , ic: tensor(0.0235) , res: tensor(0.0003) , res grad: tensor(0.0003)
save epoch: 595 , loss: tensor(0.0251) , ic: tensor(0.0219) , res: tensor(0.0003) , res grad: tensor(0.0003)
... RAR mean res:  tensor(0.0090)
... RES add [x,t]: tensor([[102.6833]]) tensor([[1.0055]]) . max res value:  tensor([0.1030])
save epoch: 603 , loss: tensor(0.0235) , ic: tensor(0.0205) , res: tensor(0.0003) , res grad: tensor(0.0003)
save epoch: 604 , loss: tensor(0.0221) , ic: tensor(0.0188) , res: tensor(0.0003) , res grad: tensor(0.0004)
save epoch: 609 , loss: tensor(0.0210) , ic: tensor(0.0171) , res: tensor(0.0003) , res grad: tensor(0.0005)
save epoch: 617 , loss: tensor(0.0198) , ic: tensor(0.0164) , res: tensor(0.0003) , res grad: tensor(0.0004)
save epoch: 618 , loss: tensor(0.0186) , ic: tensor(0.0147) , res: tensor(0.0003) , res grad: tensor(0.0005)
save epoch: 627 , loss: tensor(0.0174) , ic: tensor(0.0134) , res: tensor(0.0003) , res grad: tensor(0.0005)
save epoch: 628 , loss: tensor(0.0164) , ic: tensor(0.0129) , res: tensor(0.0002) , res grad: tensor(0.0005)
save epoch: 637 , loss: tensor(0.0149) , ic: tensor(0.0111) , res: tensor(0.0002) , res grad: tensor(0.0005)
save epoch: 645 , loss: tensor(0.0140) , ic: tensor(0.0098) , res: tensor(0.0003) , res grad: tensor(0.0006)
save epoch: 654 , loss: tensor(0.0126) , ic: tensor(0.0085) , res: tensor(0.0002) , res grad: tensor(0.0006)
save epoch: 663 , loss: tensor(0.0118) , ic: tensor(0.0078) , res: tensor(0.0002) , res grad: tensor(0.0006)
save epoch: 673 , loss: tensor(0.0109) , ic: tensor(0.0073) , res: tensor(0.0002) , res grad: tensor(0.0005)
save epoch: 684 , loss: tensor(0.0100) , ic: tensor(0.0064) , res: tensor(0.0002) , res grad: tensor(0.0005)
save epoch: 695 , loss: tensor(0.0093) , ic: tensor(0.0058) , res: tensor(0.0002) , res grad: tensor(0.0005)
... RAR mean res:  tensor(0.0074)
... RES add [x,t]: tensor([[102.2383]]) tensor([[1.0181]]) . max res value:  tensor([0.1159])
save epoch: 707 , loss: tensor(0.0088) , ic: tensor(0.0053) , res: tensor(0.0002) , res grad: tensor(0.0005)
save epoch: 720 , loss: tensor(0.0082) , ic: tensor(0.0048) , res: tensor(0.0002) , res grad: tensor(0.0005)
save epoch: 736 , loss: tensor(0.0075) , ic: tensor(0.0044) , res: tensor(0.0002) , res grad: tensor(0.0005)
save epoch: 748 , loss: tensor(0.0070) , ic: tensor(0.0040) , res: tensor(0.0002) , res grad: tensor(0.0005)
save epoch: 764 , loss: tensor(0.0067) , ic: tensor(0.0038) , res: tensor(0.0002) , res grad: tensor(0.0004)
save epoch: 770 , loss: tensor(0.0063) , ic: tensor(0.0034) , res: tensor(0.0001) , res grad: tensor(0.0004)
save epoch: 786 , loss: tensor(0.0059) , ic: tensor(0.0030) , res: tensor(0.0001) , res grad: tensor(0.0004)
save epoch: 800 , loss: tensor(0.0056) , ic: tensor(0.0029) , res: tensor(0.0001) , res grad: tensor(0.0004)
... RAR mean res:  tensor(0.0056)
... RES add [x,t]: tensor([[102.2931]]) tensor([[1.0077]]) . max res value:  tensor([0.1060])
save epoch: 816 , loss: tensor(0.0053) , ic: tensor(0.0026) , res: tensor(0.0001) , res grad: tensor(0.0004)
save epoch: 848 , loss: tensor(0.0047) , ic: tensor(0.0021) , res: tensor(0.0001) , res freq: tensor(0.0004)
[p_net train complete]
best pnet epoch:  848 , loss:  tensor(0.0047) , train time:  30.848557949066162
max abs e1(x,0):  0.048368715767721907
e1net epoch: 0 ,loss: tensor(9.9061) ,ic loss: tensor(8.0199) ,res: tensor(0.3295) ,res grad: tensor(0.0477)
... RAR mean res:  tensor(0.5488)
... RES add [x,t]: tensor([[102.1085]]) tensor([[1.0093]]) . max res value:  tensor([0.9730])
e1net epoch: 1 ,loss: tensor(5.6252) ,ic loss: tensor(4.4682) ,res: tensor(0.1893) ,res grad: tensor(0.0422)
e1net epoch: 2 ,loss: tensor(2.8073) ,ic loss: tensor(2.1154) ,res: tensor(0.0994) ,res grad: tensor(0.0390)
e1net epoch: 3 ,loss: tensor(1.1762) ,ic loss: tensor(0.7589) ,res: tensor(0.0455) ,res grad: tensor(0.0380)
e1net epoch: 4 ,loss: tensor(0.4774) ,ic loss: tensor(0.1976) ,res: tensor(0.0175) ,res grad: tensor(0.0385)
e1net epoch: 16 ,loss: tensor(0.4173) ,ic loss: tensor(0.1807) ,res: tensor(0.0079) ,res grad: tensor(0.0394)
e1net epoch: 17 ,loss: tensor(0.3819) ,ic loss: tensor(0.1496) ,res: tensor(0.0074) ,res grad: tensor(0.0390)
e1net epoch: 89 ,loss: tensor(0.3628) ,ic loss: tensor(0.1484) ,res: tensor(0.0065) ,res grad: tensor(0.0364)
... RAR mean res:  tensor(0.0502)
... RES add [x,t]: tensor([[102.1029]]) tensor([[1.0050]]) . max res value:  tensor([0.7241])
e1net epoch: 1211 ,loss: tensor(0.3446) ,ic loss: tensor(0.1459) ,res: tensor(0.0067) ,res grad: tensor(0.0331)
... RAR mean res:  tensor(0.0487)
... RES add [x,t]: tensor([[102.0717]]) tensor([[1.0027]]) . max res value:  tensor([0.6537])
e1net epoch: 1333 ,loss: tensor(0.3272) ,ic loss: tensor(0.1401) ,res: tensor(0.0066) ,res grad: tensor(0.0308)
... RAR mean res:  tensor(0.0497)
... RES add [x,t]: tensor([[102.1238]]) tensor([[1.0032]]) . max res value:  tensor([0.6328])
e1net epoch: 1492 ,loss: tensor(0.3106) ,ic loss: tensor(0.1341) ,res: tensor(0.0064) ,res grad: tensor(0.0289)
... RAR mean res:  tensor(0.0476)
... RES add [x,t]: tensor([[100.5710]]) tensor([[1.0036]]) . max res value:  tensor([0.6325])
e1net epoch: 1705 ,loss: tensor(0.2950) ,ic loss: tensor(0.1277) ,res: tensor(0.0062) ,res grad: tensor(0.0272)
e1net epoch: 1800 ,loss: tensor(0.2800) ,ic loss: tensor(0.1221) ,res: tensor(0.0064) ,res grad: tensor(0.0252)
... RAR mean res:  tensor(0.0413)
... RES add [x,t]: tensor([[100.5874]]) tensor([[1.0025]]) . max res value:  tensor([0.6406])
e1net epoch: 1882 ,loss: tensor(0.2655) ,ic loss: tensor(0.1135) ,res: tensor(0.0068) ,res grad: tensor(0.0236)
... RAR mean res:  tensor(0.0402)
... RES add [x,t]: tensor([[102.1512]]) tensor([[1.0077]]) . max res value:  tensor([0.6389])
e1net epoch: 1937 ,loss: tensor(0.2508) ,ic loss: tensor(0.1042) ,res: tensor(0.0068) ,res grad: tensor(0.0225)
e1net epoch: 1968 ,loss: tensor(0.2378) ,ic loss: tensor(0.0952) ,res: tensor(0.0068) ,res grad: tensor(0.0217)
e1net epoch: 1987 ,loss: tensor(0.2246) ,ic loss: tensor(0.0889) ,res: tensor(0.0061) ,res grad: tensor(0.0211)
... RAR mean res:  tensor(0.0422)
... RES add [x,t]: tensor([[101.9757]]) tensor([[1.0074]]) . max res value:  tensor([0.5686])
e1net epoch: 2007 ,loss: tensor(0.2123) ,ic loss: tensor(0.0795) ,res: tensor(0.0059) ,res grad: tensor(0.0206)
e1net epoch: 2025 ,loss: tensor(0.2014) ,ic loss: tensor(0.0720) ,res: tensor(0.0059) ,res grad: tensor(0.0200)
e1net epoch: 2040 ,loss: tensor(0.1882) ,ic loss: tensor(0.0633) ,res: tensor(0.0055) ,res grad: tensor(0.0195)
e1net epoch: 2053 ,loss: tensor(0.1787) ,ic loss: tensor(0.0561) ,res: tensor(0.0055) ,res grad: tensor(0.0191)
e1net epoch: 2065 ,loss: tensor(0.1697) ,ic loss: tensor(0.0496) ,res: tensor(0.0053) ,res grad: tensor(0.0187)
e1net epoch: 2100 ,loss: tensor(0.1582) ,ic loss: tensor(0.0410) ,res: tensor(0.0050) ,res grad: tensor(0.0185)
... RAR mean res:  tensor(0.0413)
... RES add [x,t]: tensor([[102.0658]]) tensor([[1.0103]]) . max res value:  tensor([0.4418])
e1net epoch: 2115 ,loss: tensor(0.1500) ,ic loss: tensor(0.0378) ,res: tensor(0.0048) ,res grad: tensor(0.0176)
e1net epoch: 2132 ,loss: tensor(0.1418) ,ic loss: tensor(0.0318) ,res: tensor(0.0048) ,res grad: tensor(0.0173)
e1net epoch: 2149 ,loss: tensor(0.1344) ,ic loss: tensor(0.0274) ,res: tensor(0.0046) ,res grad: tensor(0.0168)
e1net epoch: 2166 ,loss: tensor(0.1275) ,ic loss: tensor(0.0234) ,res: tensor(0.0044) ,res grad: tensor(0.0164)
e1net epoch: 2182 ,loss: tensor(0.1209) ,ic loss: tensor(0.0198) ,res: tensor(0.0043) ,res grad: tensor(0.0160)
e1net epoch: 2197 ,loss: tensor(0.1148) ,ic loss: tensor(0.0168) ,res: tensor(0.0041) ,res grad: tensor(0.0155)
... RAR mean res:  tensor(0.0414)
... RES add [x,t]: tensor([[102.0477]]) tensor([[1.0011]]) . max res value:  tensor([0.2917])
e1net epoch: 2240 ,loss: tensor(0.1085) ,ic loss: tensor(0.0147) ,res: tensor(0.0039) ,res grad: tensor(0.0149)
e1net epoch: 2255 ,loss: tensor(0.1030) ,ic loss: tensor(0.0125) ,res: tensor(0.0036) ,res grad: tensor(0.0145)
e1net epoch: 2269 ,loss: tensor(0.0978) ,ic loss: tensor(0.0110) ,res: tensor(0.0034) ,res grad: tensor(0.0139)
e1net epoch: 2282 ,loss: tensor(0.0928) ,ic loss: tensor(0.0104) ,res: tensor(0.0032) ,res grad: tensor(0.0133)
e1net epoch: 2294 ,loss: tensor(0.0882) ,ic loss: tensor(0.0095) ,res: tensor(0.0029) ,res grad: tensor(0.0128)
... RAR mean res:  tensor(0.0365)
... RES add [x,t]: tensor([[99.9935]]) tensor([[1.0522]]) . max res value:  tensor([0.3047])
e1net epoch: 2309 ,loss: tensor(0.0834) ,ic loss: tensor(0.0086) ,res: tensor(0.0027) ,res grad: tensor(0.0122)
e1net epoch: 2323 ,loss: tensor(0.0792) ,ic loss: tensor(0.0078) ,res: tensor(0.0025) ,res grad: tensor(0.0118)
e1net epoch: 2337 ,loss: tensor(0.0751) ,ic loss: tensor(0.0072) ,res: tensor(0.0023) ,res grad: tensor(0.0113)
e1net epoch: 2349 ,loss: tensor(0.0714) ,ic loss: tensor(0.0067) ,res: tensor(0.0021) ,res grad: tensor(0.0109)
e1net epoch: 2372 ,loss: tensor(0.0665) ,ic loss: tensor(0.0063) ,res: tensor(0.0018) ,res grad: tensor(0.0102)
e1net epoch: 2386 ,loss: tensor(0.0627) ,ic loss: tensor(0.0059) ,res: tensor(0.0017) ,res grad: tensor(0.0097)
... RAR mean res:  tensor(0.0255)
... RES add [x,t]: tensor([[99.0870]]) tensor([[1.0037]]) . max res value:  tensor([0.3049])
e1net epoch: 2499 ,loss: tensor(0.0576) ,ic loss: tensor(0.0094) ,res: tensor(0.0013) ,res grad: tensor(0.0083)
... RAR mean res:  tensor(0.0242)
... RES add [x,t]: tensor([[99.5090]]) tensor([[1.0055]]) . max res value:  tensor([0.2486])
e1net epoch: 2529 ,loss: tensor(0.0543) ,ic loss: tensor(0.0090) ,res: tensor(0.0011) ,res grad: tensor(0.0080)
e1net epoch: 2541 ,loss: tensor(0.0514) ,ic loss: tensor(0.0080) ,res: tensor(0.0011) ,res grad: tensor(0.0076)
e1net epoch: 2553 ,loss: tensor(0.0486) ,ic loss: tensor(0.0070) ,res: tensor(0.0010) ,res grad: tensor(0.0073)
e1net epoch: 2567 ,loss: tensor(0.0461) ,ic loss: tensor(0.0064) ,res: tensor(0.0010) ,res grad: tensor(0.0070)
e1net epoch: 2583 ,loss: tensor(0.0437) ,ic loss: tensor(0.0057) ,res: tensor(0.0009) ,res grad: tensor(0.0067)
e1net epoch: 2600 ,loss: tensor(0.0414) ,ic loss: tensor(0.0052) ,res: tensor(0.0009) ,res grad: tensor(0.0064)
... RAR mean res:  tensor(0.0167)
... RES add [x,t]: tensor([[100.5794]]) tensor([[1.0041]]) . max res value:  tensor([0.2034])
e1net epoch: 2630 ,loss: tensor(0.0392) ,ic loss: tensor(0.0050) ,res: tensor(0.0009) ,res grad: tensor(0.0060)
e1net epoch: 2650 ,loss: tensor(0.0371) ,ic loss: tensor(0.0045) ,res: tensor(0.0008) ,res grad: tensor(0.0057)
e1net epoch: 2673 ,loss: tensor(0.0352) ,ic loss: tensor(0.0042) ,res: tensor(0.0007) ,res grad: tensor(0.0055)
e1net epoch: 2700 ,loss: tensor(0.0335) ,ic loss: tensor(0.0039) ,res: tensor(0.0007) ,res grad: tensor(0.0052)
... RAR mean res:  tensor(0.0137)
... RES add [x,t]: tensor([[100.6113]]) tensor([[1.0036]]) . max res value:  tensor([0.1668])
e1net epoch: 2742 ,loss: tensor(0.0317) ,ic loss: tensor(0.0039) ,res: tensor(0.0006) ,res grad: tensor(0.0050)
e1net epoch: 2770 ,loss: tensor(0.0300) ,ic loss: tensor(0.0035) ,res: tensor(0.0006) ,res grad: tensor(0.0047)
e1net epoch: 2800 ,loss: tensor(0.0285) ,ic loss: tensor(0.0033) ,res: tensor(0.0005) ,res grad: tensor(0.0045)
... RAR mean res:  tensor(0.0116)
... RES add [x,t]: tensor([[100.0002]]) tensor([[1.0416]]) . max res value:  tensor([0.1605])
e1net epoch: 2861 ,loss: tensor(0.0271) ,ic loss: tensor(0.0035) ,res: tensor(0.0005) ,res grad: tensor(0.0042)
e1net epoch: 2885 ,loss: tensor(0.0257) ,ic loss: tensor(0.0030) ,res: tensor(0.0005) ,res grad: tensor(0.0041)
... RAR mean res:  tensor(0.0106)
... RES add [x,t]: tensor([[100.0248]]) tensor([[1.0597]]) . max res value:  tensor([0.1327])
e1net epoch: 2913 ,loss: tensor(0.0244) ,ic loss: tensor(0.0025) ,res: tensor(0.0004) ,res grad: tensor(0.0039)
e1net epoch: 2941 ,loss: tensor(0.0231) ,ic loss: tensor(0.0023) ,res: tensor(0.0004) ,res grad: tensor(0.0038)
... RAR mean res:  tensor(0.0097)
... RES add [x,t]: tensor([[101.4779]]) tensor([[1.2412]]) . max res value:  tensor([0.1089])
e1net epoch: 3003 ,loss: tensor(0.0219) ,ic loss: tensor(0.0023) ,res: tensor(0.0004) ,res grad: tensor(0.0035)
e1net epoch: 3024 ,loss: tensor(0.0208) ,ic loss: tensor(0.0020) ,res: tensor(0.0004) ,res grad: tensor(0.0034)
e1net epoch: 3048 ,loss: tensor(0.0197) ,ic loss: tensor(0.0018) ,res: tensor(0.0004) ,res grad: tensor(0.0032)
e1net epoch: 3071 ,loss: tensor(0.0187) ,ic loss: tensor(0.0016) ,res: tensor(0.0003) ,res grad: tensor(0.0031)
... RAR mean res:  tensor(0.0371)
... RES add [x,t]: tensor([[101.4998]]) tensor([[1.2695]]) . max res value:  tensor([0.1525])
e1net epoch: 3141 ,loss: tensor(0.0178) ,ic loss: tensor(0.0019) ,res: tensor(0.0003) ,res grad: tensor(0.0028)
e1net epoch: 3151 ,loss: tensor(0.0169) ,ic loss: tensor(0.0016) ,res: tensor(0.0003) ,res grad: tensor(0.0028)
e1net epoch: 3165 ,loss: tensor(0.0159) ,ic loss: tensor(0.0014) ,res: tensor(0.0003) ,res grad: tensor(0.0026)
e1net epoch: 3181 ,loss: tensor(0.0151) ,ic loss: tensor(0.0012) ,res: tensor(0.0003) ,res grad: tensor(0.0025)
e1net epoch: 3197 ,loss: tensor(0.0143) ,ic loss: tensor(0.0011) ,res: tensor(0.0003) ,res grad: tensor(0.0024)
... RAR mean res:  tensor(0.0085)
... RES add [x,t]: tensor([[103.4836]]) tensor([[1.1700]]) . max res value:  tensor([0.0983])
e1net epoch: 3214 ,loss: tensor(0.0136) ,ic loss: tensor(0.0010) ,res: tensor(0.0003) ,res grad: tensor(0.0023)
e1net epoch: 3231 ,loss: tensor(0.0129) ,ic loss: tensor(0.0009) ,res: tensor(0.0003) ,res grad: tensor(0.0021)
... RAR mean res:  tensor(0.0084)
... RES add [x,t]: tensor([[103.5681]]) tensor([[1.2706]]) . max res value:  tensor([0.0976])
e1net epoch: 3303 ,loss: tensor(0.0122) ,ic loss: tensor(0.0010) ,res: tensor(0.0002) ,res grad: tensor(0.0020)
e1net epoch: 3316 ,loss: tensor(0.0115) ,ic loss: tensor(0.0009) ,res: tensor(0.0002) ,res grad: tensor(0.0019)
e1net epoch: 3331 ,loss: tensor(0.0109) ,ic loss: tensor(0.0008) ,res: tensor(0.0002) ,res grad: tensor(0.0018)
e1net epoch: 3350 ,loss: tensor(0.0104) ,ic loss: tensor(0.0007) ,res: tensor(0.0002) ,res grad: tensor(0.0017)
e1net epoch: 3372 ,loss: tensor(0.0098) ,ic loss: tensor(0.0007) ,res: tensor(0.0002) ,res grad: tensor(0.0016)
e1net epoch: 3397 ,loss: tensor(0.0093) ,ic loss: tensor(0.0007) ,res: tensor(0.0002) ,res grad: tensor(0.0015)
... RAR mean res:  tensor(0.0070)
... RES add [x,t]: tensor([[103.4655]]) tensor([[1.1866]]) . max res value:  tensor([0.0937])
e1net epoch: 3428 ,loss: tensor(0.0089) ,ic loss: tensor(0.0006) ,res: tensor(0.0002) ,res grad: tensor(0.0015)
e1net epoch: 3499 ,loss: tensor(0.0084) ,ic loss: tensor(0.0007) ,res: tensor(0.0002) ,res grad: tensor(0.0014)
... RAR mean res:  tensor(0.0064)
... RES add [x,t]: tensor([[103.5104]]) tensor([[1.2066]]) . max res value:  tensor([0.0958])
e1net epoch: 3551 ,loss: tensor(0.0079) ,ic loss: tensor(0.0007) ,res: tensor(0.0002) ,res grad: tensor(0.0013)
e1net epoch: 3576 ,loss: tensor(0.0075) ,ic loss: tensor(0.0007) ,res: tensor(0.0002) ,res grad: tensor(0.0012)
... RAR mean res:  tensor(0.0061)
... RES add [x,t]: tensor([[103.5150]]) tensor([[1.2329]]) . max res value:  tensor([0.0886])
e1net epoch: 3665 ,loss: tensor(0.0070) ,ic loss: tensor(0.0007) ,res: tensor(0.0002) ,res grad: tensor(0.0011)
... RAR mean res:  tensor(0.0059)
... RES add [x,t]: tensor([[103.5216]]) tensor([[1.2294]]) . max res value:  tensor([0.0854])
e1net epoch: 3706 ,loss: tensor(0.0066) ,ic loss: tensor(0.0007) ,res: tensor(0.0002) ,res grad: tensor(0.0010)
e1net epoch: 3787 ,loss: tensor(0.0062) ,ic loss: tensor(0.0007) ,res: tensor(0.0002) ,res grad: tensor(0.0009)
... RAR mean res:  tensor(0.0057)
... RES add [x,t]: tensor([[103.5416]]) tensor([[1.2665]]) . max res value:  tensor([0.0805])
e1net epoch: 3889 ,loss: tensor(0.0058) ,ic loss: tensor(0.0007) ,res: tensor(0.0002) ,res grad: tensor(0.0009)
... RAR mean res:  tensor(0.0056)
... RES add [x,t]: tensor([[103.4631]]) tensor([[1.1879]]) . max res value:  tensor([0.0812])
e1net epoch: 3988 ,loss: tensor(0.0054) ,ic loss: tensor(0.0007) ,res: tensor(0.0002) ,res grad: tensor(0.0008)
... RAR mean res:  tensor(0.0055)
... RES add [x,t]: tensor([[103.4512]]) tensor([[1.1860]]) . max res value:  tensor([0.0769])
e1net epoch: 4034 ,loss: tensor(0.0052) ,ic loss: tensor(0.0007) ,res: tensor(0.0002) ,res grad: tensor(0.0007)
e1net epoch: 4073 ,loss: tensor(0.0050) ,ic loss: tensor(0.0007) ,res: tensor(0.0002) ,res grad: tensor(0.0007)
[e1_net train complete]
best e1net epoch:  4073 , loss:  tensor(0.0050) , train time:  598.4285380840302
t1= 2.0 , a1 [Monte]= [0.16862039]
t1= 4.0 , a1 [Monte]= [0.29038224]
t1= 6.0 , a1 [Monte]= [0.4302794]
